Process for Microsatellite Instability Detection

ABSTRACT

The invention provides methods for determining the MSI status of a patient by liquid biopsy with sample preparation using hybrid capture and non-unique barcodes. In certain aspects, the invention provides a method of detecting microsatellite instability (MSI). The method includes obtaining cell-free DNA (cfDNA) from a sample of blood or plasma from a patient and sequencing portions of the cfDNA to obtain sequences of a plurality of tracts of nucleotide repeats in the cfDNA. A report is provided describing an MSI status in the patient when a distribution of lengths of the plurality of tracts has peaks that deviate significantly from peaks in a reference distribution.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Ser. No. 16/204,642, filedNov. 29, 2018, which claims benefit of priority under 35 U.S.C. § 119(e)of U.S. Ser. No. 62/593,664 filed Dec. 1, 2017, and of U.S. Ser. No.62/741,448 filed Oct. 4, 2018. The entire content of each of theaforementioned applications is herein incorporated by reference for allpurposes.

TECHNICAL FIELD

The present invention relates generally to the detection, monitoring,and treatment of cancer and more specifically to determining the MSIstatus of a patient by liquid biopsy.

BACKGROUND

Cancer causes more than a half a million deaths each year in the UnitedStates alone. The success of current treatments depends on the type ofcancer and the stage at which it is detected. Many treatments includecostly and painful surgeries and chemotherapies, and are oftenunsuccessful. Early and accurate detection of mutations is essential foreffective cancer therapy.

Many cancers involve the accumulation of mutations that results fromfailure of the DNA mismatch-repair (MMR). One important marker of MMRdeficiency is microsatellite instability (MSI), a polymorphism of tandemnucleotide repeat lengths ubiquitously distributed throughout thegenome. The presence of MMR-deficiency or MSI may serve as a marker forimmunotherapy response with checkpoint inhibition. Knowledge of MSIstatus is thus important and valuable for the treatment of cancer. Whileit may be possible to determine MSI status by sequencing DNA from atumor sample, such as a formalin-fixed paraffin-embedded (FFPE) tumortissue specimen, there are patients for whom tumor material is notreadily obtained.

Absent a fixed tissue specimen, a potential source for tumor informationis through the analysis of circulating tumor DNA (ctDNA). ctDNA isreleased from tumor tissue into the blood and can be analyzed by liquidbiopsy. Liquid biopsies potentially allow for the detection andcharacterization of cancer. However, liquid biopsies present their owninherent challenges associated with low circulating tumor DNA (ctDNA)levels as well as problems with faithfully amplifying and sequencingregions of DNA characterized by tracts of mononucleotide repeats.

SUMMARY OF THE INVENTION

The present invention is based on the seminal discovery that acirculating tumor DNA based approach is useful for the detection of hightumor mutation burden and microsatellite instability in cancer patientswith advanced disease and can be used to predict responders to immunecheckpoint blockade.

The invention provides methods for determining the MSI status of apatient by liquid biopsy. Methods include a sample preparation usinghybrid capture and non-unique barcodes. The sample preparation bothcompensates for errors such as sequencing artifacts and polymeraseslippage and provides for the successful capture of target DNA even whenpresent only at a very low fraction of total DNA. Methods includesequencing tracts of mononucleotide repeats within captured sample andmodelling the distribution of lengths of those tracts. A peak-findingoperation evaluates peaks in the modelled distribution and reveals MSIin the patient when the peaks deviate from a reference distribution(e.g., such as by indicating that the tracts of mononucleotide repeatsin the patient's DNA are markedly shorter than in healthy DNA).

Methods of the disclosure are amenable to implementation in conjunctionwith other genomic screenings such as screening panels of markers,genes, or whole genomes to report mutations or mutational burden.Methods may be implemented by including MSI markers within any suitableliquid-biopsy based sequencing assay and may evaluate MSI status byinterrogating MSI markers such as BAT-25, BAT-26, MONO-27, NR-21, andNR-24, BAT-40, TGFβ RII, IGFIIR, hMSH3, BAX and dinucleotide D2S123,D9S283, D9S1851 and D18S58 loci, by way of example, or by modelingdistributions of lengths of any other suitable set(s) of repeats in thegenome.

In certain aspects, the invention provides a method of detectingmicrosatellite instability (MSI). The method includes obtainingcell-free DNA (cfDNA) from a sample of plasma from a patient andsequencing portions of the cfDNA to obtain sequences of a plurality oftracts of nucleotide repeats in the cfDNA. A report is provideddescribing an MSI status in the patient when a distribution of lengthsof the plurality of tracts has peaks that deviate significantly frompeaks in a reference distribution. Obtaining the cfDNA may includecapturing target portions of DNA with probes, fragmenting the targetportions to yield fragments, and attaching barcodes to the fragments. Inpreferred embodiments, the barcodes are non-unique barcodes that includeduplicates such that different ones of the fragments are attached toidentical barcodes.

The method may include amplifying the fragments to produce ampliconsthat include barcode information and copies of the fragments, whereinthe sequencing step comprises sequencing the amplicons. In one aspect,the sequencing is next-generation, short-read sequencing. The obtainedsequences may include a plurality of sequence reads and the method mayinclude aligning the sequence reads to a reference, and identifyinggroups of sequence reads that originated from a unique segment of thecfDNA by means of the barcode information and position or content of thesequence reads.

The use of the non-unique barcodes to identify groups of sequence readsthat originated from a unique segment of the cfDNA allows for thelengths of the plurality of tracts to be determined correctly bycorrecting for errors introduced by sequencing artifacts or polymeraseslippage during the amplifying step.

Preferably, the target portions are markers for MSI such as one or moreof BAT25, BAT26, MON027, NR21, NR24, Penta C, and Penta D. For example,the markers may include all of BAT25, BAT26, MON027, NR21, and NR24. Incertain embodiments, each of the microsatellite markers is selected fromthe group consisting of BAT-25, BAT-26, MONO-27, NR-21, NR-24, Penta C,and Penta D, BAT-40, TGFβ RII, IGFIIR, hMSH3, BAX and dinucleotideD2S123, D9S283, D9S1851 and D18S58 loci, by way of example.

In some embodiments, the method includes recommending a treatment forthe patient based on the MSI status. Where the MSI status indicates thatthe patient is microsatellite instable, the treatment may include animmune checkpoint inhibitor. In certain embodiments, the method includesadministering the treatment (e.g., the immune checkpoint inhibitor) tothe patient. The immune checkpoint inhibitor may be, for example, anantibody such as an anti-PD-1 antibody; an anti-IDO antibody;anti-CTLA-4 antibody; an anti-PD-L1 antibody; or an anti-LAG-3 antibody.

Related aspects provide a method of detecting microsatellite instability(MSI) that includes obtaining a sample comprising fragments of cell-freeDNA from a patient; attaching barcodes to the fragments, wherein atleast some of the barcodes are not unique; sequencing the barcodes toobtain sequences of a plurality of markers in the DNA; determining adistribution of lengths of the plurality of markers; and providing areport describing MSI in the patient when peaks in the distributiondeviate significantly from expected peaks in a modeled healthydistribution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 diagrams a method for determining MSI status.

FIG. 2 shows a system for performing methods of the invention.

FIG. 3 shows a model of length distribution of mononucleotide repeats.

FIG. 4 shows a report provided by systems and methods of the invention.

FIGS. 5A-5D Plasma-Based Detection of Microsatellite Instability. (A)Prior to error correction and Digital Peak Finding (DPF)(light pink),the mononucleotide count distribution demonstrated high background noisedue to sequencing related aberrations and polymerase slippage in librarypreparation PCR and sequencing. These are subsequently resolved aftererror correction and DPF (dark pink) to create distinct distributionsfor MSI and MSS alleles. (B) Across the BAT25, BAT26, MON027, NR21, andNR24 mononucleotide loci in 163 healthy donor plasma specimens, theerror corrected mononucleotide count distribution was assessed with aDPF algorithm to identify mononucleotide alleles and determine MSIstatus. Prior to error correction and DPF (light pink), the majority ofhealthy donor samples exhibit alleles below the MSI cutoff (hashedline). Kaplan-Meier curves for progression free survival (C) and overallsurvival (D) among patients with progressive metastatic carcinoma weredetermined using MSI status from pre-treatment plasma specimens. In MSIpatients (n=9*), median progression free survival was 16.17 months,while median overall survival was not reached. In MSS patients (n=7*),median progression free survival and median overall survival were 2.81and 7.6 months, respectively. *Three patients with a tissue enrollmentstatus of MSI-H were classified as MSS using pre-treatment baselinecfDNA obtained from plasma.

FIGS. 6A-6E Plasma-Based Detection of High Tumor Mutation Burden. (A)Using whole exome sequencing data derived from The Cancer Genome Atlas(TCGA), a significant positive correlation between the tumor mutationburden (TMB) evaluated in the 98 kb targeted regions compared to thewhole exome analyses was observed (r=0.91, p<0.0001; Pearsoncorrelation). (B) Comparison of the accuracy for determination of theTMB derived from the targeted panel in plasma compared to whole-exomeanalyses of matched archival tissue samples in 13 patients yielded asignificant positive correlation (r=0.693, p=0.007; Pearsoncorrelation). (C) The overall TMB status at baseline was assigned asTMB-High or TMB-Low using a cutoff of 50.8 mutations/Mbp sequenced. Intotal, six patients were categorized as TMB-High and ten patients asTMB-Low, with a median load of 132 mutations/Mbp sequenced and 15.2mutations/Mbp sequenced, respectively. Additionally, 163 healthy donorcases were evaluated, all of which were determined to be TMB-Low, with amedian load of 0 mutations/Mbp sequenced across the panel. Kaplan-Meiercurves for progression free survival (D) and overall survival (E) amongthis same cohort of patients were determined using TMB status frompre-treatment plasma specimens with a cutoff of 50.8 mutations/Mbpsequenced. In TMB-High patients (n=6), median progression free survivaland median overall survival were not reached. In TMB-Low patients(n=10), median progression free survival and median overall survivalwere 2.84 and 7.62 months, respectively.

FIGS. 7A-7F Serial Plasma-Based Overall Survival Analysis for PatientsTreated with Immune Checkpoint Blockade. (A) Evaluation of overallsurvival with the protein biomarker level at last dose (CEA or CA19-9).A significant inverse correlation was observed between the overallsurvival in months when compared to the residual protein biomarker(r=−0.99, p=<0.001; Pearson correlation). (B) Kaplan-Meier curves foroverall survival among patients with tissue enrollment status of MSI anddetectable protein biomarker levels (n=8). For patients with >80%reduction in protein biomarker levels (n=4), median overall survival wasnot reached. For patients with ≤80% reduction in protein biomarkerlevels (n=4), median overall survival was 5.26 months. (C) Evaluation ofoverall survival compared to residual MSI allele levels at last dose. Asignificant inverse correlation was observed between the overallsurvival when compared to the residual MSI allele levels (r=−0.70,p=0.034; Pearson correlation). (D) Kaplan-Meier curves for overallsurvival among patients with tissue enrollment status of MSI anddetectable MSI status at baseline (n=9). For patients with twoconsecutive timepoints displaying no residual MSI alleles (n=4) medianoverall survival was not reached. For patients with multiple timepointscontaining residual MSI alleles (n=5) median overall survival was 7.64months. (E) Evaluation of overall survival compared to residual TMBlevels at last dose. A significant inverse correlation was observedbetween the overall survival in months when compared to the residual TMBlevels (r=−0.95, p=<0.001; Pearson correlation). (F) Kaplan-Meier curvesfor overall survival among patients with tissue enrollment status of MSIand detectable TMB levels at baseline (n=11). For patients with >90%reduction in TMB levels (n=4), median overall survival was not reached.For patients with ≤90% reduction in TMB levels (n=7), median overallsurvival was 7.64 months. “/” indicates a censored datapoint; “*”indicates cases where baseline protein biomarker, MSI or TMB was notdetected and were not included in the subsequent analyses; In caseswhere residual protein biomarker, MSI or TMB levels increased whencompared to baseline, values of greater than 100% are indicated.

FIGS. 8A-8D Monitoring of Patients During Immune Checkpoint Blockade.For three patients with a complete response to immune checkpointblockade (CS97 (A), CS98 (B), and CS00 (C) and one patient withprogressive disease (C505 (D)), circulating protein biomarkers (CEA,ng/mL and CA19-9, units/mL), residual alleles exhibiting MSI, and TMBlevels were evaluated over time during treatment. In each caseexhibiting a complete response, residual MSI and TMB alleles werereduced to 0% mutant allele fraction (MAF) between 0.6 and 4.8 monthsafter first dose.

FIGS. 9A-9D Archival Tissue-Based Detection of MicrosatelliteInstability and High Tumor Mutation Burden. Kaplan-Meier curves forprogression free survival (A) and overall survival (B) among patientswith progressive metastatic carcinoma were determined using MSI statusfrom archival tissue. In MSI patients (n=12), median progression freesurvival and median overall survival were 4.23 and 20.69 months,respectively. In MSS patients (n=4), median progression free survivaland median overall survival were 2.81 and 6.31 months, respectively.Kaplan-Meier curves for progression free survival (C) and overallsurvival (D) among patients with progressive metastatic carcinoma weredetermined. In TMB-High patients (n=10), median progression freesurvival was 10.81 months, while median overall survival was notreached. In TMB-Low patients (n=3), median progression free survival andmedian overall survival were 2.81 and 5.02 months, respectively.

FIGS. 10A-10F Plasma-Based Progression Free Survival Analysis forPatients Treated with Immune Checkpoint Blockade. (A) Evaluation ofprogression free survival with the protein biomarker level at last dose(CEA or CA19-9). An inverse correlation was observed between theprogression free survival in months when compared to the residualprotein biomarker (r=−0.92, p=0.001; Pearson correlation). (B)Kaplan-Meier curves for progression free survival among patients withtissue enrollment status of MSI and detectable protein biomarker levels(n=8). For patients with >80% reduction in protein biomarker levels(n=4), median progression free survival was not reached. For patientswith ≤80% reduction in protein biomarker levels (n=4), medianprogression free survival was 2.63 months. (C) Evaluation of progressionfree survival compared to residual MSI allele levels at last dose. Asignificant inverse correlation was observed between the progressionfree survival in months when compared to the residual MSI allele levels(r=−0.84, p=0.004; Pearson correlation). (D) Kaplan-Meier curves forprogression free survival among patients with tissue enrollment statusof MSI and detectable MSI status at baseline (n=9). For patients withtwo consecutive timepoints displaying no residual MSI alleles (n=4)median progression free survival was not reached. For patients withmultiple timepoints containing residual MSI alleles (n=5) medianprogression free survival was 3.01 months. (E) Evaluation of progressionfree survival compared to residual TMB levels at last dose. Asignificant inverse correlation was observed between the progressionfree survival in months when compared to the residual TMB levels(r=−0.98, p=<0.001; Pearson correlation). (F) Kaplan-Meier curves forprogression free survival among patients with tissue enrollment statusof MSI and detectable TMB levels at baseline (n=11). For patientswith >90% reduction in TMB levels (n=4), median progression freesurvival was not reached. For patients with ≤90% reduction in TMB levels(n=7), median progression free survival was 2.88 months. “/” indicates acensored datapoint; “*” indicates cases where baseline proteinbiomarker, MSI or TMB was not detected and were not included in thesubsequent analyses; In cases where residual protein biomarker, MSI orTMB levels increased when compared to baseline, values of greater than100% are indicated.

FIG. 11 Radiographic Imaging of Case CS98 Displaying a Complete Responseto Immune Checkpoint Blockade. After 20 weeks of treatment with immunecheckpoint blockade, radiographic imaging was performed and revealedpotential lesions in the liver, but later disappeared, so likely insteadrepresented inflammatory liver nodules.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to the discovery that microsatelliteinstability (MSI) and high tumor mutation burden (TMB-High) arepan-tumor biomarkers used to select patients for treatment with immunecheckpoint blockade. The present invention shows a plasma-based approachfor detection of MSI and TMB-High in patients with advanced cancer. Todetect sequence alterations across a 98 kilobase panel, including thosein microsatellite regions, the inventors developed an error correctionapproach with specificities >99% (n=163) and sensitivities of 75% (n=12)and 60% (n=10), respectively, for MSI and TMB-High. For patients treatedwith PD-1 blockade, the data demonstrate that MSI and TMB-High inpre-treatment plasma predicted progression-free survival (hazard ratios0.2 and 0.12, p=0.01 and 0.004, respectively). The data shows theresults when plasma during therapy was analyzed in order to develop aprognostic signature for patients who achieved durable response to PD-1blockade. These analyses demonstrate the feasibility of non-invasivepan-cancer screening and monitoring of patients who exhibit MSI orTMB-High and have a high likelihood of responding to immune checkpointblockade.

The disclosure provides for the detection of MSI by liquid biopsy. Whileplasma is the illustrative example provided herein, it is understoodthat a liquid biopsy can be performed with a biological sample includingblood, plasma, saliva, urine, feces, tears, mucosal secretions and otherbiological fluids.

In particular, methods of the disclosure provide and include theanalytical validation of an integrated NGS-based liquid biopsy approachfor the detection of microsatellite instability associated with cancerssuch as pancreatic, colon, gastric, endometrial, cholangiocarcinoma,breast, lung, head and neck, kidney, bladder, or prostate cancer, aswell as hematopoietic cancers, among others. Failure of the DNA mismatchrepair (MMR) pathway during DNA replication in cancer leads to theincreased accumulation of somatic mutations. One important marker of MMRdeficiency is microsatellite instability (MSI), which presents aspolymorphism of tandem nucleotide repeat lengths ubiquitouslydistributed throughout the genome. Methods of the disclosure are offeredto assay for and detect those markers via liquid biopsy. Additionally,since the presence of MMR-deficiency or MSI may serve as a marker forimmunotherapy response with checkpoint inhibition, methods may be usedto determine a course of treatment such as immunotherapy or theadministration of a checkpoint inhibitor.

Microsatellite instability (MSI) and mismatch repair (MMR) deficiencyhave recently been demonstrated to predict immune checkpoint blockaderesponse. The checkpoint inhibitor pembrolizumab is now indicated forthe treatment of adult and pediatric patients with any unresectable ormetastatic solid tumors identified as having either of these biomarkers.This indication covers patients with solid tumors that have progressedfollowing prior treatment and have no satisfactory alternative treatmentoptions.

Cancer is characterized by the accumulation of somatic mutations thathave the potential to result in the expression of neoantigens, which mayelicit T-cell-dependent immune responses against tumors. MMR is amechanism by which post-replicative mismatches in daughter DNA strandsare repaired and replaced with the correct DNA sequence. MMR deficiencyresults in both MSI and high tumor mutation burden (TMB-High), whichincreases the likelihood that acquired somatic mutations may betranscribed and translated into proteins that are recognized asimmunogenic neoantigens. Historically, testing for MSI has beenrestricted to screening for Hereditary Non-Polyposis Colorectal Cancer(HNPCC), which is often characterized by early age onset colorectalcancer and endometrial cancer, as well as other extracolonic tumors.HNPCC, commonly referred to as Lynch Syndrome, is caused by mutations inthe DNA mismatch repair genes (MLH1, MSH2, MSH6 and PMS2), as well asthe more recently described, EPCAM (16). In addition to familialconditions, MSI can occur sporadically in cancer, and both hereditaryand sporadic MSI patients respond to immune checkpoint blockade (1,2). Arecent study, conducted across 39 tumor types and 11,139 patients todetermine the landscape of MSI prevalence, concluded that 3.8% of thesecancers across 27 tumor types displayed MSI, including 31.4% ofuterine/endometrial carcinoma, 19.7% of colon adenocarcinoma, and 19.1%of stomach adenocarcinoma.

MSI can be detected through alterations in the length of microsatellitesequences typically due to deletions of repeating units of DNA to createnovel allele lengths in tumor-derived DNA when compared to amatched-normal or a reference population. Current methods for MSItesting, using tissue biopsies and resection specimens, includePCR-based amplification followed by capillary electrophoresis, and morerecently, next-generation sequencing (NGS) based approaches, which areused to quantify microsatellite allele lengths. The challenge associatedwith application of the former approach are polymerase induced errors(stutter bands), particularly in samples with low tumor purity, such ascell-free DNA (cfDNA), which can mask true biological alleles exhibitingMSI. In the case of NGS based approaches, sensitivity is typicallylimited by the accuracy for determination of homopolymer lengths. Anovel method was recently described for determination of MSI usingpre-PCR elimination of wild-type DNA homopolymers in liquid biopsies.However, given the low prevalence of MSI across cancer, it would bepreferable to develop an NGS profiling approach which can include otherclinically actionable alterations in cancer, including TMB, sequencemutations, copy number alterations, and translocations.

In addition to the technical challenges associated with MSI detection,it is often not possible to readily obtain biopsy or resection tissuefor genetic testing due to insufficient material (biopsy size and tumorcellularity), exhaustion of the limited material available after priortherapeutic stratification, logistical considerations for tumor andnormal sample acquisition after initial diagnosis, or safety concernsrelated to additional tissue biopsy interventions (26). In contrast,plasma-based approaches offer the unique opportunity to obtain a rapidand real-time view of the primary tumor and metastatic lesions alongwith associated response to therapy. Circulating tumor DNA can be usedto monitor and assess residual disease in response to clinicalintervention, such as surgery or chemotherapy (27-33), which candirectly impact patient care. To determine the clinical impact ofidentifying tumors that harbor MSI or TMB-High using cfDNA, we developedand applied a 98 kb 58-gene targeted panel to cancer patients withadvanced disease treated with PD-1 blockade. FIG. 1 diagrams a method101 of detecting microsatellite instability (MSI). The method 101includes obtaining 107 cell-free DNA (cfDNA) from a sample of plasmafrom a patient. Preferably, non-unique barcode are attached 111.Portions of the cfDNA are sequenced 115 to obtain sequences of aplurality of tracts of nucleotide repeats in the cfDNA. The method 101includes modeling 121 a distribution of lengths of tracts of nucleotiderepeats. A report is provided 125 describing an MSI status in thepatient when a distribution of lengths of the plurality of tracts haspeaks that deviate significantly from peaks in a reference distribution.Obtaining the cfDNA may include capturing target portions of DNA withprobes, fragmenting the target portions to yield fragments, andattaching barcodes to the fragments.

Briefly, cell-free DNA may be extracted from cell line or blood orplasma specimens and prepared into a genomic library suitable fornext-generation sequencing with oligonucleotide barcodes throughend-repair, A-tailing and adapter ligation. An in-solution hybridcapture, utilizing for example, 120 base-pair (bp) RNA oligonucleotidesmay be performed.

In one embodiment, at least about 10-100 ng, such as 50 ng of DNA in 100microliters of TE is fragmented in a sonicator to a size of about150-450 bp. To remove fragments smaller than 150 bp, DNA may be purifiedusing Agencourt AMPure XP beads (Beckman Coulter, Ind.) in a ratio of1.0 to 0.9 of PCR product to beads twice and, e.g., washed using 70%ethanol per the manufacturer's instructions. Purified, fragmented DNA ismixed with H2O, End Repair Reaction Buffer, End Repair Enzyme Mix (cat#E6050, NEB, Ipswich, Mass.). The mixture is incubated then purifiedusing Agencourt AMPure XP beads (Beckman Coulter, Ind.) in a ratio of1.0 to 1.25 of PCR product to beads and washed using 70% ethanol per themanufacturer's instructions. To A-tail, end-repaired DNA is mixed withTailing Reaction Buffer and Klenow (exo-) (cat #E6053, NEB, Ipswich,Mass.). The mixture is incubated at 37 degree C. for 30 min and purifiedusing Agencourt AMPure XP beads (Beckman Coulter, Ind.) in a ratio of1.0 to 1.0 of PCR product to beads and washed using 70% ethanol per themanufacturer's instructions. For adaptor ligation, A-tailed DNA is mixedwith H2O, PE-adaptor (Illumina), Ligation buffer and Quick T4 DNA ligase(cat #E6056, NEB, Ipswich, Mass.). The ligation mixture was incubated,then amplified.

Exonic or targeted regions were captured in solution using the AgilentSureSelect v.4 kit according to the manufacturer's instructions(Agilent, Santa Clara, Calif.). The captured library was then purifiedwith a Qiagen MinElute column purification kit. To purify PCR products,a NucleoSpin Extract II purification kit (Macherey-Nagel, PA) may beused before sequencing.

Targeted sequencing is performed. Two technical challenges toimplementing these approaches in the form of a liquid biopsy include thelimited amount of DNA obtained and the low mutant allele frequencyassociated with the MSI markers. It may be that as few as severalthousand genomic equivalents are obtained per milliliter of plasma, andthe mutant allele frequency can range from <0.01% to >50% total cfDNA.see Bettegowda, 2014, Detection of circulating tumor DNA in early- andlate-stage human malignancies, Sci Trans Med 6(224): 224ra24,incorporated by reference. The disclosed techniques overcome suchproblems and improve test sensitivity, optimized methods for conversionof cell-free DNA into a genomic library, and digital sequencingapproaches to improve the specificity of next-generation sequencingapproaches.

Methods may include extracting and isolating cell-free DNA from a bloodor plasma sample and assigning an exogenous barcode to each fragment togenerate a DNA library. The exogenous barcodes are from a limited poolof non-unique barcodes, for example 8 different barcodes. The barcodedfragments are differentiated based on the combination of their exogenousbarcode and the information about the reads that results from sequencingsuch as the sequence of the reads (effectively, an endogenous barcode)or position information (e.g., stop and/or start position) of the readmapped to a reference. The DNA library is redundantly sequenced 115 andthe sequences with matching barcodes are reconciled. The reconciledsequences may be aligned to a human genome reference.

The invention recognizes that completely unique barcode sequences areunnecessary. Instead, a combination of predefined set of non-uniquesequences together with the endogenous barcodes can provide the samelevel of sensitivity and specificity that unique barcodes could forbiologically relevant DNA amounts and can, in-fact, correct forsequencing artifacts or polymerase slippage. A limited pool of barcodesis more robust than a conventional unique set and easier to create anduse. Methods include obtaining a sample comprising nucleic acidfragments, providing a plurality of sets of non-unique barcodes, andtagging 111 the nucleic acid fragments with the barcodes to generate agenomic library, wherein each nucleic acid fragment is tagged with thesame barcode as another different nucleic acid fragment in the genomiclibrary.

In embodiments, the plurality of sets is limited to twenty or fewerunique barcodes. In other embodiments, the plurality of sets is limitedto ten or fewer unique barcodes.

According to the present invention, a small pool of non-unique exogenousbarcodes can be used to provide a robust assay that achieves levels ofsensitivity that are comparable to traditional, more complex barcodingschemes, while vastly reducing cost and complication.

After processing steps such as those described above, nucleic acids canbe sequenced. Sequencing may be by any method known in the art. DNAsequencing techniques include classic dideoxy sequencing reactions(Sanger method) using labeled terminators or primers and gel separationin slab or capillary, and next generation sequencing methods such assequencing by synthesis using reversibly terminated labeled nucleotides,pyrosequencing, 454 sequencing, Illumina/Solexa sequencing, allelespecific hybridization to a library of labeled oligonucleotide probes,sequencing by synthesis using allele specific hybridization to a libraryof labeled clones that is followed by ligation, real time monitoring ofthe incorporation of labeled nucleotides during a polymerization step,polony sequencing, and SOLiD sequencing. Separated molecules may besequenced by sequential or single extension reactions using polymerasesor ligases as well as by single or sequential differentialhybridizations with libraries of probes.

A sequencing technique that can be used includes, for example, use ofsequencing-by-synthesis systems sold under the trademarks GS JUNIOR, GSFLX+and 454 SEQUENCING by 454 Life Sciences, a Roche company (Branford,Conn.), and described by Margulies, M. et al., Genome sequencing inmicro-fabricated high-density picotiter reactors, Nature, 437: 376-380(2005); U.S. Pat. Nos. 5,583,024; 5,674,713; and 5,700,673, the contentsof which are incorporated by reference herein in their entirety.

Other examples of DNA sequencing techniques include SOLiD technology byApplied Biosystems from Life Technologies Corporation (Carlsbad, Calif.)and ion semiconductor sequencing using, for example, a system sold underthe trademark ION TORRENT by Ion Torrent by Life Technologies (South SanFrancisco, Calif.). Ion semiconductor sequencing is described, forexample, in Rothberg, et al., An integrated semiconductor deviceenabling non-optical genome sequencing, Nature 475: 348-352 (2011); U.S.Pub. 2010/0304982; U.S. Pub. 2010/0301398; U.S. Pub. 2010/0300895; U.S.Pub. 2010/0300559; and U.S. Pub. 2009/0026082, the contents of each ofwhich are incorporated by reference in their entirety.

Another example of a sequencing technology that can be used is Illuminasequencing. Illumina sequencing is based on the amplification of DNA ona solid surface using fold-back PCR and anchored primers. Adapters areadded to the 5′ and 3′ ends of DNA that is either naturally orexperimentally fragmented. DNA fragments that are attached to thesurface of flow cell channels are extended and bridge amplified. Thefragments become double stranded, and the double stranded molecules aredenatured. Multiple cycles of the solid-phase amplification followed bydenaturation can create several million clusters of approximately 1,000copies of single-stranded DNA molecules of the same template in eachchannel of the flow cell. Primers, DNA polymerase and fourfluorophore-labeled, reversibly terminating nucleotides are used toperform sequential sequencing. After nucleotide incorporation, a laseris used to excite the fluorophores, and an image is captured and theidentity of the first base is recorded. The 3′ terminators andfluorophores from each incorporated base are removed and theincorporation, detection and identification steps are repeated.Sequencing according to this technology is described in U.S. Pat. Nos.7,960,120; 7,835,871; 7,232,656; 7,598,035; 6,911,345; 6,833,246;6,828,100; 6,306,597; 6,210,891; U.S. Pub. 2011/0009278; U.S. Pub.2007/0114362; U.S. Pub. 2006/0292611; and U.S. Pub. 2006/0024681, eachof which are incorporated by reference in their entirety.

Preferably sequencing is done redundantly for deep coverage, preferablyat least 30× coverage or 100×. DNA libraries may be sequenced usingpaired-end 111umina HiSeq 2500 sequencing chemistry to an average targettotal coverage of either >20,000-fold or >5,000-fold coverage for eachtargeted base. Sequence data may be mapped to the reference humangenome. Preferably, the sequencing is next-generation, short-readsequencing. The obtained sequences may include a plurality of sequencereads and the method may include aligning the sequence reads to areference, and identifying groups of sequence reads that originated froma unique segment of the cfDNA by means of the barcode information andposition or content of the sequence reads. Primary processing ofsequence data may be performed using Illumina CASAVA software (v1.8),including masking of adapter sequences. Sequence reads may bealignedagainst the human reference genome (version hg18) using ELAND withadditional realignment of select regions using the Needleman-Wunschmethod.

In some embodiments, the barcodes are non-unique barcodes that includeduplicates such that different ones of the fragments are attached toidentical barcodes. The high clinical efficacy of MSI status nowrequires a fast, objective, highly sensitive screening method,particularly in late-stage patients where tumor material may not bereadily obtained. However, to extend this approach to a liquid biopsypanel requires technological advances to both overcome the inherentchallenges associated with low circulating tumor DNA (ctDNA) levelswhich is compounded by polymerase slippage in mononucleotide repeatregions during PCR amplification as well as other sequencing artifacts.

To overcome these limitations, we applied error correction approachusing molecular barcoding together with high sequencing depth and anovel peak finding algorithm to more accurately identify the specificmononucleotide sequences in cell-free DNA (cfDNA) analyses of a 64 genepanel, by way of illustration. The MSI markers can be sequenced inconjunction with such 64 gene panel, or in isolation (e.g., justsequence the markers) or in conjunction with any other gene panel(e.g., >300 genes) or with whole genome or whole exome sequencing.

The method may include amplifying the fragments to produce ampliconsthat include barcode information and copies of the fragments, whereinthe sequencing step comprises sequencing the amplicons.

The use of the non-unique barcodes to identify groups of sequence readsthat originated from a unique segment of the cfDNA allows for thelengths of the plurality of tracts to be determined correctly bycorrecting for errors introduced by sequencing artifacts or polymeraseslippage during the amplifying step. By eliminating a significantmajority of sequencing errors and polymerase slippage artifacts, we wereable to reduce background error rates by >90%. Combined withimplementation of a distribution modeling and a peak finding algorithm,we were able to accurately sequence the mononucleotide tracts tominimize false discovery rates for cfDNA analyses.

FIG. 2 shows a system 901 for performing methods of the disclosure. Thesystem 901 includes a computer 933, and may optionally include a servercomputer 909. In certain embodiments, the system 901 includes asequencing instrument 955 (such as an Illumina HiSeq device) which mayitself include an instrument computer 951 (e.g., onboard in thesequencing instrument). Any of the computers may communicate via network915. Each computer preferably includes at least one tangible,non-transitory memory device 975 and any input/output devices coupled toa processor. The memory may include instructions executable by theprocessor(s) to perform methods such as a method of detectingmicrosatellite instability (MSI) that includes obtaining a samplecomprising fragments of cell-free DNA from a patient; attaching barcodesto the fragments, wherein at least some of the barcodes are not unique;sequencing the barcodes to obtain sequences of a plurality of markers inthe DNA; determining a distribution of lengths of the plurality ofmarkers; and providing a report describing MSI in the patient when peaksin the distribution deviate significantly from expected peaks in amodeled healthy distribution.

FIG. 3 illustrates distribution modeling for peak finding. In theillustrated distribution model 301, a model 307 of a distribution oflengths of tracts of nucleotide repeats is determined. It may becompared to a reference distribution 305 and an operation may beperformed to find a peak 313 for the patient data 307 and/or thereference distribution 305 (which may be from patient healthy sample DNAor from a human genome reference or any other suitable source. In someembodiments, when the peak finding operation determines that the patientpeak 313 is sufficiently deviant from a location of a reference peak,the method and system report the patient as MSI (microsatelliteinstable) for the relevant marker. Most preferably, the peak finding anddistribution modeling is performed for each MSI marker. A benefit of thedescribed method is that the distribution modeling and peak finding maybe reliably implemented and automated in a high-throughput system.

MSI may be assayed by hybrid capture and NGS to address such markers asmononucleotide repeat markers such as BAT25, BAT26, MON027, NR21, andNR24. See U.S. Pub. 2017/0267760, incorporated by reference. Knowledgeof MSI status is important and valuable in the treatment of manycancers, and there are patients for whom tumor material is not readilyobtained. Tumors deficient in mismatch repair are particularlysusceptible to a particular form of immunotherapy because this phenotyperesults in ongoing accumulation of mutations at a high frequency.Methods may include recommending or administering treatment for cancerpatients that display the microsatellite instability phenotype or otherhigh mutational burden. The treatment involves an inhibitory antibodyfor an immune checkpoint. Such checkpoints include PD-1, IDO, CTLA-4,PD-L1, and LAG-3 by way of example. Other immune checkpoints can be usedas well. Antibodies can be administered by any means that is convenient,including but not limited to intravenous infusion, oral administration,subcutaneous administration, sublingual administration, ocularadministration, nasal administration, and the like.

Preferably, the method 101 includes providing 125 a report with MSIstatus.

FIG. 4 shows a report 410 that includes a status of “instable” forcertain MSI markers. Preferably, the target portions are markers for MSIsuch as one or more of BAT25, BAT26, MON027, NR21, NR24, Penta C, andPenta D. For example, the markers may include all of BAT25, BAT26,MON027, NR21, and NR24. In certain embodiments, each of themicrosatellite markers is selected from the group consisting of BAT-25,BAT-26, MONO-27, NR-21, NR-24, Penta C, and Penta D.

In some embodiments, the method includes recommending a treatment forthe patient based on the MSI status. Where the MSI status indicates thatthe patient is microsatellite instable, the treatment may include animmune checkpoint inhibitor. In certain embodiments, the method includesadministering the treatment (e.g., the immune checkpoint inhibitor) tothe patient. The immune checkpoint inhibitor may be, for example, anantibody such as an anti-PD-1 antibody; an anti-IDO antibody;anti-CTLA-4 antibody; an anti-PD-L1 antibody; or an anti-LAG-3 antibody.Types of antibodies which can be used include any that are developed forthe immune checkpoint inhibitors. These can be monoclonal or polyclonal.They may be single chain fragments or other fragments of fullantibodies, including those made by enzymatic cleavage or recombinantDNA techniques. They may be of any isotype, including but not limited toIgG, IgM, IgE. The antibodies may be of any species source, includinghuman, goat, rabbit, mouse, cow, chimpanzee. The antibodies may behumanized or chimeric. The antibodies may be conjugated or engineered tobe attached to another moiety, whether a therapeutic molecule or atracer molecule. The therapeutic molecule may be a toxin, for example.The present invention is more particularly described in the followingexamples which are intended as illustrative only since numerousmodifications and variations therein will be apparent to those skilledin the art. The following examples are intended to illustrate but notlimit the invention.

EXAMPLES Example 1 Methods Patients and Sample Collection

Formalin fixed paraffin embedded (FFPE) tumor and matched normal buffycoat specimens (n=61) from individuals with cancer were obtained aftersurgical resection through commercial biorepositories from BioIVT(Hicksville, N.Y., USA), Indivumed (Hamburg, Germany), and iSpecimen(Lexington, Mass., USA). Plasma samples from healthy individuals (n=163)were procured through BioIVT (Hicksville, N.Y., USA) during routinescreening with negative results and no prior history of cancer. Humancells from previously characterized MSI cell lines were obtained fromATCC (Manassas, Va., USA) (n=5; LS180, LS411N, SNU-C2B, RKO, andSNU-C2A). Finally, baseline and serial plasma samples from cancerpatients with progressive metastatic carcinoma (n=16; 11 colorectal, 3ampullary, and 2 small intestine) were obtained while patients wereenrolled in a phase 2 clinical trial to evaluate immune checkpointblockade with pembrolizumab (1,2). Radiographic and serum proteinbiomarker data for CEA and CA19-9 were collected as a part of routineclinical care. All samples were obtained under Institutional ReviewBoard approved protocols with informed consent for research.

Orthogonal Testing of FFPE Tissue for MSI Status

The Promega MSI analysis system (Madison, Wis., USA) was used to assessMSI status in DNA derived from FFPE tumor tissue together with matchednormal buffy coat by multiplex PCR and fluorescent capillaryelectrophoresis. Tumors were classified as MSI if two or more of thefive mononucleotide markers (BAT25, BAT26, MON027, NR21, and NR24) hadsignificant length differences compared to the matched normal allelelengths. Additionally, 2-pentanucleotide repeat loci (PentaC and PentaD)were used to confirm case identity between normal and tumor samples.

Sample Preparation and Next-Generation Sequencing FFPE Tumor and NormalAnalyses

Sample processing from tissue or buffy coat, library preparation, hybridcapture, and sequencing were performed as previously described atPersonal Genome Diagnostics (Baltimore, Md.) (34,36). Briefly, DNA wasextracted from FFPE tissue and matched normal buffy coat cells using theQiagen FFPE Tissue Kit and DNA Blood Mini Kit, respectively (Qiagen,Hilden, Germany). Genomic DNA was sheared using a Covaris sonicator(Woburn, Mass., USA) to a size range of 150-450 bp, and subsequentlyused to generate a genomic library using the New England Biolabs(Ipswich, Mass., USA) end-repair, A-tailing, and adapter ligationmodules. Finally, genomic libraries were amplified and captured usingthe Agilent SureSelect XT in-solution hybrid capture system with acustom 120 bp RNA panel targeting the pre-defined regions of interestacross 125 genes (Table 1). Captured libraries were sequenced on theIllumina HiSeq 2000 or 2500 (Illumina, San Diego, Calif., USA) with 100bp paired end reads.

Plasma Analyses

Sample processing from plasma, library preparation, hybrid capture, andsequencing were performed as previously described at Personal GenomeDiagnostics (Baltimore, Md.) (34). Briefly, blood was collected in EDTAtubes and centrifuged at 800 g for 10 minutes at 4° C. to separateplasma from white blood cells. Cell-free DNA was extracted from plasmausing the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany).Libraries were prepared with 5-250 ng of cfDNA using the NEBNext DNALibrary Prep Kit (New England Biolabs, Ipswich, Mass., USA). After endrepair and a-tailing, a pool of eight unique Illumina dual indexadapters with 8 bp barcodes were ligated to cfDNA to allow for accurateerror correction of duplicate reads, followed by 12 cycles ofamplification. Targeted hybrid capture was performed using AgilentSureSelect XT in-solution hybrid capture system with a custom 120 bp RNApanel targeting the pre-defined regions of interest across 58 genes(Table 4) according to the manufacturer protocol (Agilent Technologies,Santa Clara, Calif., USA). Captured libraries were sequenced on theIllumina HiSeq 2000 or 2500 (Illumina, San Diego, Calif., USA) with 100bp paired end reads.

Example 2 Microsatellite Instability Analyses by Next-GenerationSequencing

Sequence data were aligned to the human reference genome assembly (hg19)using BWA-MEM (37). Reads mapping to microsatellites were excised usingSamtools (38) and analyzed for insertion and deletion events (indels).In most cases, alignment and variant calling did not generate accurateindel calls in repeated regions due to low quality bases surrounding themicrosatellites. Therefore, a secondary local realignment and indelquantitation was performed. Reads were considered for an expanded indelanalysis if (i) the mononucleotide repeat was contained to more thaneight bases inside of the start and end of the read, (ii) the indellength was <12 bases from the reference length, (iii) there were nosingle base changes found within the repeat region, (iv) the read had amapping score of 60, and (v) ≤20 bases of the read were soft clipped foralignment. After read specific mononucleotide length analysis, errorcorrection was performed to allow for an aggregated and accuratequantitation among duplicated fragments using molecular barcoding. Readswere aggregated into barcode families by using the ordered and combinedread 1 and read 2 alignment positions with the molecular barcode.Barcode families were considered for downstream analysis if theycomprised of at least 2 reads and >50% of reads had consistentmononucleotide lengths. The error corrected mononucleotide lengthdistribution was subjected to a peak finding algorithm where localmaxima were required to be greater than the error corrected distinctfragment counts of the adjacent lengths ±2 bp. Identified peaks werefurther filtered to only include those which had >3 error correcteddistinct fragments at ≥1% of the absolute coverage. The shortestidentified mononucleotide allele length was compared to the hg19reference length. If the allele length was ≥3 bp shorter than thereference length, the given mononucleotide loci was classified asexhibiting instability. This approach was applied across allmononucleotide loci. Samples were classified as MSI-H if ≥20% of lociwere MSI. In the targeted 58 gene plasma panel, BAT25, BAT26, MON027,NR21, and NR24 mononucleotide loci were for the determination of MSIstatus. In the targeted 125 gene targeted tissue panel, an additional 65microsatellite regions were used for MSI classification.

Example 3 Tumor Mutational Burden Analyses by Next-Generation Sequencing

Next generation sequencing data were processed and variants wereidentified using the VariantDx custom software as previously described(34). A final set of candidate somatic mutations were selected for tumormutational burden analyses based on: (i) variants enriched due tosequencing or alignment error were removed (≤5 observations or <0.30%mutant allele fraction), (ii) nonsynonymous and synonymous variants wereincluded, but variants arising in non-coding regions were removed, (iii)hotspot variants annotated in COSMIC (version 72) were not included toreduce bias toward driver alterations, (iv) common germline SNPs foundin dbSNP (version 138) were removed as well as variants deemed privategermline variants based on the variant allele frequency, and (v)variants associated with clonal hematopoietic expansion were notincluded in the candidate variant set (39).

In Silico TCGA Analyses

In order to evaluate the accuracy of the 98 kb targeted panel forprediction of TMB, a comparison to whole-exome sequencing data derivedfrom The Cancer Genome Atlas (TCGA) (35) was performed by consideringsynonymous and nonsynonymous alterations, excluding known hotspotmutations which may not be representative of TMB in the tumor. Thecutoff for consideration as TMB-High was set to 5 candidate variants(50.8 mutations/Mbp sequenced) based on in silico analyses utilizing theTCGA data to achieve >95% accuracy (>36 mutations/Mbp).

Statistical Analyses

Due to small sample size, Firth's Penalized Likelihood was used toevaluate significant differences between Kaplan-Meier curves forprogression free survival and overall survival with the classifiersbaseline MSI status, baseline TMB status, two consecutive timepointswith >80% reduction in baseline protein biomarker levels, twoconsecutive timepoints with 0% residual MSI alleles on treatment, andtwo consecutive timepoints with >90% reduction in baseline TMB levels.Pearson correlations were used to evaluate significant associationbetween TMB in the 58 gene targeted panel compared to whole-exomeanalyses, progression free and overall survival compared to residualprotein biomarker levels, and progression free and overall survivalcompared to residual MSI and TMB allele levels. A student t-test wasused to evaluate significant differences between the mean TMB level inTMB-High and TMB-Low patients. Response rate was calculated as thenumber of patients exhibiting a complete or partial response as aproportion of the total patients considered, and then evaluated using aFisher's exact test.

Example 4 Development of an Assay to Identify MSI in Cell-Free DNA

To identify MSI in tumor-derived cfDNA, a method to detect lengthpolymorphisms in mononucleotide tract alleles in circulating tumor DNA(ctDNA), which occur at low frequency in plasma, is needed. To overcomethis issue, we developed a highly sensitive error-correction approachincorporating the commonly-used mononucleotide tracts BAT25, BAT26,MON027, NR21, and NR24 for the determination of MSI status in tissue andplasma specimens using NGS. DNA was converted into an NGS compatiblelibrary using molecular barcoding, after which these targetedmicrosatellite loci were enriched using in-solution hybrid capturechemistry together with the regions associated with other clinicallyrelevant genomic alterations.

To address the technical challenges associated with detection of lowlevel allele length polymorphisms obtained from NGS, we combined anerror correction approach for accurate determination of insertions anddeletions (indels) present in the cfDNA fragments, together with adigital peak finding (DPF) method for quantification of MSI and MSSalleles. Redundant sequencing of each cfDNA fragment was performed, andreads were aligned to the five microsatellite loci contained in thehuman reference genome (hg19). cfDNA sequences were then analyzed forindels through a secondary local alignment at these five microsatelliteloci to more accurately determine the indel length. To perform the errorcorrection, duplicated reads associated with each cfDNA molecule wereconsolidated, only recognizing indels present throughout barcoded DNAfragment replicates obtained through redundant sequencing. Finally, theDPF approach was applied across the error corrected distribution ofindels to identify high confidence alleles which exhibit microsatelliteinstability (FIGS. 5A and 5B).

To demonstrate the capability of this approach, we first evaluated theperformance of the method for detection of MSI in formalin fixed,paraffin embedded (FFPE) tumor tissue specimens obtained from 31MSI-High (MSI-H) and 30 microsatellite stable (MSS) tumors previouslycharacterized with the PCR-based Promega MSI analysis system. Inaddition to these five mononucleotide markers, we sequenced 125 selectedcancer genes which harbor clinically actionable genetic alterationsconsisting of sequence mutations (single base substitutions and indels),copy number alterations, and gene rearrangements in cancer (Table 1).Analyses of these 61 colorectal tumors yielded 193 Gb of total sequencedata, corresponding to 832-fold distinct coverage on average across the979 kb panel (Table 2). Analysis of these five mononucleotide loci,together with 65 additional microsatellite regions contained within the125 gene panel resulted in 100% sensitivity ( 31/31) and 100%specificity ( 30/30) for determination of MSI status using thepatient-matched tumor and normal samples (Table 3). Similarly, analysisof tumor NGS data using the DPF approach without the patient-matchednormal sample yielded 100% concordance ( 61/61).

Next, we evaluated the signal-to-noise ratio in homopolymer regions fromnext-generation sequencing data obtained using cfDNA extracted fromplasma. Together with the five mononucleotide loci, we developed a 98kb, 58 gene panel for sequence mutation (single base substitutions andindels) analyses of clinically actionable genetic alterations in cancer(Table 4). To demonstrate the specificity of this approach for directdetection of MSI, we first obtained plasma from healthy donors (n=163),all of which would be expected to be tumor-free and MSS. These analysesyielded over 1.2 Tb of total sequence data, corresponding to 2,600-folddistinct coverage on average across the 98 kb targeted panel, andresulted in a per-patient specificity of 99.4% ( 162/163) fordetermination of MSI status (FIG. 5B, Tables 5 and 6).

Because ctDNA, even in patients with advanced cancer, may be present atmutant allele fractions (MAFs) less than 5%, we characterized theability of DPF for sensitive and reproducible detection of MSI at lowMAFs. Five previously characterized MSI cell line samples obtained fromATCC (LS180, LS411N, SNU-C2B, RKO, and SNU-C2A) were sheared to afragment profile simulating cfDNA and diluted with normal DNA to yield atotal of 25 ng evaluated at 1% MAF. Additionally, three of these celllines (LS180, LS411N, and SNU-C2B) were evaluated at 1% MAF intriplicate within, and triplicate across library preparation andsequencing runs (Table 5). Based on the MAF observed in the parentalcell line, the cases detected as MSI were computationally confirmed tocontain MSI allele MAFs of 0.35%-1.87%, with a median MSI allele MAF of0.92%. In total, MSI was detected in 90% ( 18/20) of samples anddemonstrated 93.3% ( 14/15) repeatability and reproducibility within andacross runs (Table 6). For one case which was not detected as MSI, oneMSI allele was identified at 0.33% MAF and for the other case, no MSIalleles were detected.

Example 5 Assessment of MSI in CFDNA in Patients Treated With PD-1Blockade

To evaluate the analytical and clinical performance of this approach fordetermination of MSI in cfDNA from patients with late-stage cancers, weobtained baseline and serial plasma from patients with metastaticcancers (including 11 colorectal, 3 ampullary, and 2 small intestine),with or without MMR deficiency, while enrolled in a clinical trial toevaluate immune checkpoint blockade with the PD-1 blocking antibody,pembrolizumab (1,2) (Table 7). In total, 12 MSI-H cases and 4 MSS cases,determined through archival tissue-based analyses, were evaluated acrossat least two timepoints, including baseline, and after approximately 2weeks, 10 weeks, 20 weeks, and >100 weeks.

Patients with MSI tumors as determined by archival tissue analyses hadimproved progression-free survival (hazard ratio, 0.25; p=0.05,likelihood ratio test) and overall survival (hazard ratio, 0.24;p=0.041, likelihood ratio test) (FIGS. 9A and 9B and Table 8). In cfDNA,we could detect MSI in 75% ( 9/12) of the previously characterized MSI-Hpatients, and correctly identified 100% ( 4/4) of the MSS patients(Table 6). Of the three cases that were MSI in the tumor tissue and MSSin the cfDNA, one was a colorectal tumor (patient exhibited progressivedisease) and two were small intestinal tumors (one patient exhibited apartial response and one exhibited progressive disease) with relativelylow levels of ctDNA with MAF of 0.4%, 1.1%, and no detectable ctDNA inthe third case (34) (Table 7).

We then evaluated pre-treatment MSI status in ctDNA to predict responseand clinical outcome to treatment with PD-1 blockade. We assessedradiographic response, progression-free and overall survival to predictclinical outcome. When compared to progression free survival, directdetection of MSI in baseline cfDNA could be used to predict response toimmune checkpoint blockade (hazard ratio, 0.2; p=0.01, likelihood ratiotest) (FIGS. 5C and 5D).

Estimating Tumor Mutation Burden in ctDNA

In addition to MSI status, we also evaluated the ability of our cfDNApanel to predict TMB across a range of tumor types, using whole exomesequencing data derived from The Cancer Genome Atlas (TCGA) (35). Weconsidered synonymous and nonsynonymous alterations identified by TCGAand excluded known hotspot mutations which may not be representative ofTMB in the tumor. These analyses demonstrated a positive correlationbetween predicted TMB from our targeted 58 gene plasma panel compared tothe TCGA whole exome analyses (r=0.91, p<0.0001; Pearson correlation)(FIG. 6A). We determined that a cutoff of five mutations (50.8mutations/Mbp sequenced) in the targeted plasma panel could be used toidentify tumors with exceptionally high TMB related to MMR deficiency(>36 mutations/Mbp) at >95% accuracy.

Patients with TMB-High tumors as determined by archival tissue analyses(≥10 mutations/Mbp) had improved progression-free survival (hazardratio, 0.19; p=0.041, likelihood ratio test) and overall survival(hazard ratio, 0.18; p=0.047, likelihood ratio test) (FIGS. 9C and 9D).We also evaluated the accuracy of TMB derived from the targeted panel in13 baseline plasma cases, compared to whole-exome analyses of tumor andmatched normal tissue in the same patients(1,2), and a similarcorrelation was identified (r=0.69, p=0.007; Pearson correlation) (FIG.6B). These patients were classified as either TMB-High or TMB-Low usinga cutoff of 50.8 mutations/Mbp sequenced, which captured six of the tentumors categorized as TMB-High by archival tissue and provided astatistically significant difference in the TMB classification(p=0.0072, t-test) (FIG. 6C). This algorithm was applied to the same 163healthy donor plasma samples and 100% ( 163/163) were determined to beTMB-Low (FIG. 6C). When considering TMB classification as a predictor ofclinical outcome from the same phase 2 study cohort, TMB-High status wasassociated with favorable progression free survival (hazard ratio, 0.12;p=0.004 likelihood ratio test) and overall survival (hazard ratio, 0.16;p=0.014, likelihood ratio test) (FIGS. 6D and 6E). Interestingly, allfour MSI-H enrolled patients exhibiting a complete response wereclassified as TMB-High, and all five enrolled MSI-H patients withprogressive disease were classified as TMB-Low (Table 7).

Example 6 Assessment of Molecular Remission and Biomarker Dynamics inPatients Treated With PD-1 Blockade

In addition to baseline plasma analyses, we also hypothesized that themolecular remission, as measured by ctDNA during treatment, would bepredictive of long term durable response to immune checkpoint blockade.We first evaluated the utility of monitoring serum tumor proteinbiomarkers CEA or CA19-9 for determination of response and found thatmultiple consecutive timepoints with a >80% reduction in the baselineprotein biomarker level resulted in improved overall and progressionfree survival (hazard ratio, 0.05; p=0.01 and hazard ratio, 0.05;p=0.01, likelihood ratio test, respectively) (FIGS. 7A and 7B and FIGS.10A and 10B). When evaluating the on-treatment serial plasma samples forresidual ctDNA levels, there was a significant inverse correlationbetween the overall and progression free survival when compared to theresidual MSI allele levels at last dose (r=−0.70, p=0.034 and r=−0.84,p=0.004, respectively; Pearson correlation) (FIGS. 7C and FIG. 10C). Wewere able to correctly identify four of the six MSI patients who wouldachieve a long term durable clinical response requiring multipleconsecutive on-treatment time points with 0% residual alleles displayingMSI, all four of which displayed a complete response (hazard ratio,0.09; p=0.032, likelihood ratio test for overall survival) (FIG. 7D andFIG. 10D). A similar trend was observed when considering patients witha >90% decrease in overall TMB across two timepoints when compared tobaseline (hazard ratio, 0.07; p=0.013, likelihood ratio test for overallsurvival) (FIGS. 7E and 7F and FIGS. 10E and 10F).

Additionally, for three patients (CS97, CS98, and CS00) with a completeresponse to immune checkpoint blockade, and one patient (CS05) without aresponse to immune checkpoint blockade, circulating protein biomarkers(CEA, ng/mL or CA19-9, units/mL) and residual alleles exhibiting MSI andTMB were evaluated over time during treatment (FIG. 8 ). In each of thepatients exhibiting a complete response, there was a concurrent decreasein the circulating protein biomarker levels, the residual MSI alleles,and TMB levels, which correlated with reduced overall tumor volume asassessed by radiographic imaging. Protein biomarker levels decreased bymore than 80% between 1.3 to 2.3 months after first dose. Residual MSIalleles and TMB levels were reduced by >90% between 0.6 and 4.8 monthsafter first dose for these three cases. However, for patient CS05 withprogressive disease, the protein biomarker levels remained relativelyconstant, but there was an increase in residual alleles exhibiting MSIand TMB of 78% and 50%, respectively, at 4.8 months. This correlatedwith a 13% increase in tumor volume as assessed by radiographic imagingat 5 months.

Patient CS97 demonstrated a partial radiographic response at 10.6months, however, achieved a 100% reduction in residual MSI and TMBlevels at 2.8 months. CS97 then went on to a complete radiographicresponse at 20.2 months (Table 7). A different patient, CS98, appearedto develop new liver lesions at 20 weeks suggestive of progressivedisease (FIG. 11 ). However, following an initial spike, proteinbiomarkers and residual MSI and TMB levels demonstrated a biochemicaltumor response at 1.3 and 4.8 months. A liver biopsy demonstrated onlyinflammatory changes in the location where new lesions were noted,suggesting checkpoint therapy induced inflammation. Radiographic imagingfinally demonstrated resolution of any hepatic lesions and a 100%reduction in tumor volume at 16.8 months. A similar pattern was observedfor patient CS00 where significant reduction in protein biomarker andresidual MSI and TMB levels occurred at 1.5 and 0.6 months,respectively, however, radiographic imaging did not demonstrate a 100%reduction in tumor volume until 17 months. These data suggest that theresidual MSI allele burden and TMB prognostic signature are indicativeof overall tumor response to immune checkpoint blockade.

Discussion

The checkpoint inhibitor pembrolizumab is now indicated for thetreatment of adult and pediatric patients with unresectable ormetastatic solid tumors identified as having MSI or MMR deficiency(1,2). This represents the first pan-cancer biomarker indication, andnow covers patients with solid tumors that have progressed followingprior treatment and have no satisfactory alternative treatment options,as well as patients with colorectal cancer that have progressedfollowing treatment with certain chemotherapy drugs. However, it isoften not possible to readily obtain biopsy or resection tissue forgenetic testing due to insufficient material, exhaustion of the limitedmaterial available after prior therapeutic stratification, logisticalconsiderations for tumor and normal sample acquisition after initialdiagnosis, or safety concerns related to additional tissue biopsyinterventions (26).

We have described the development of a method for simultaneous detectionof MSI and TMB-High directly from cfDNA and demonstrated proof ofconcept for the clinical utility afforded through these analyses for theprediction of response to immune checkpoint blockade. Additionally,given the concordance with circulating protein biomarker data whilethese patients were on treatment, these data suggest that the residualMSI allele burden and TMB prognostic signature could be applied to othertumor types where standardized protein biomarkers do not exist and maybe an earlier predictor of response than radiographic imaging.

These methods described herein provide feasibility for a viablediagnostic approach for screening and monitoring of patients who exhibitMSI or TMB-High and may respond to immune checkpoint blockade.

TABLE 1 125 Gene List for FFPE Tissue Analyses Sequence Trans- Amplifi-Gene Mutations locations cations (n = 125) (n = 117) (n = 29) (n = 41)ABL1 Yes — — AKT1 Yes — Yes ALK Yes Yes Yes AR Yes — Yes ATM Yes — —ATRX Yes — — AXL Yes Yes Yes BCL2 Yes Yes Yes BCR — Yes — BRAF Yes YesYes BRCA1 Yes Yes — BRCA2 Yes Yes — CBFB — Yes — CCND1 Yes — Yes CCND2Yes — Yes CCND3 Yes — Yes CDK4 Yes — Yes CDK6 Yes — Yes CDKN2A Yes — —CHEK2 Yes — — CREBBP Yes — — CSF1R Yes — Yes CTNNB1 Yes — — DDR2 Yes — —DNMT3A Yes — — EGFR Yes Yes Yes EP300 Yes — — EPHA2 Yes — — ERBB2 Yes —Yes ERBB3 Yes — Yes ERBB4 Yes — — ERCC3 Yes — — ERG Yes Yes — ESR1 Yes —— ETV1 — Yes — ETV4 — Yes — ETV5 — Yes — ETV6 — Yes — EWSR1 — Yes — EZH2Yes — — FANCA Yes — — FANCD2 Yes — — FANCG Yes — — FBXW7 Yes — — FGFR1Yes Yes Yes FGFR2 Yes Yes Yes FGFR3 Yes Yes Yes FGFR4 Yes — Yes FLT1 Yes— Yes FLT3 Yes — Yes FLT4 Yes — Yes FOXL2 Yes — — GNA11 Yes — — GNAQ Yes— — GNAS Yes — — HDAC2 Yes — — HNF1A Yes — — HRAS Yes — — IDH1 Yes — —IDH2 Yes — — JAK1 Yes — — JAK2 Yes — Yes JAK3 Yes — — KDR Yes — YesKEAP1 Yes — — KIT Yes — Yes KMT2A Yes — — KRAS Yes — Yes MAP2K1 Yes — —MAP2K2 Yes — — MEN1 Yes — — MET Yes — Yes MLH1 Yes — — MLH3 Yes — — MPLYes — — MRE11A Yes — — MSH2 Yes — — MSH6 Yes — — MST1R Yes — Yes MTORYes — — MYC Yes Yes Yes MYCN Yes — Yes MYD88 Yes — — NBN Yes — — NF1 Yes— — NOTCH1 Yes — — NPM1 Yes — — NRAS Yes — — NTRK1 Yes Yes Yes NTRK2 YesYes Yes NTRK3 Yes Yes Yes PALB2 Yes — — PDGFRA Yes Yes Yes PDGFRB YesYes Yes PIK3CA Yes — Yes PIK3CB Yes — Yes PIK3R1 Yes — — PMS2 Yes — —POLD1 Yes — — POLE Yes — — PTCH1 Yes — — PTEN Yes — — PTPN11 Yes — —RAD51 Yes — — RAF1 Yes Yes — RARA Yes Yes — RB1 Yes — — RET Yes Yes YesRNF43 Yes — — ROS1 Yes Yes Yes RUNX1 Yes — Yes SDHB Yes — — SMAD4 Yes —— SMARCB1 Yes — — SMO Yes — — SRC Yes — — STK11 Yes — — TERT Yes — —TET2 Yes — — TMPRSS2 — Yes — TP53 Yes — — TSC1 Yes — — TSC2 Yes — —VEGFA Yes — Yes VHL Yes — —

TABLE 2 Summary of Next Generation Sequencing Statistics DPF for FFPETumor and Matched Normal Samples Orthogonal Matched Bases Promega TumorDPF Mapped to Average Average MSI and Tumor Bases Targeted TotalDistinct Analysis Normal Only Case Sample Tumor Tumor Bases Mapped toRegions of Coverage Coverage System Analysis Analysis ID Type TypePurity Sequenced Genome Interest (Fold) (Fold) Result Result Result T1Tumor Colorectal 40% 3,166,928,200 2,938,394,500 1,273,077,559 1,258 927MSI-H MSI-H MSI-H Cancer T2 Tumor Colorectal 40% 2,956,194,8002,731,020,900 1,118,791,878 1,102 769 MSI-H MSI-H MSI-H Cancer T3 TumorColorectal 80% 4,620,105,200 4,266,153,700 1,719,208,838 1,705 1,151MSI-H MSI-H MSI-H Cancer T4 Tumor Colorectal 60% 3,830,551,4003,577,875,700 1,587,036,697 1,584 1,047 MSI-H MSI-H MSI-H Cancer T5Tumor Colorectal 60% 3,694,440,800 3,417,070,000 1,421,110,311 1,4231,021 MSI-H MSI-H MSI-H Cancer T6 Tumor Colorectal 70% 2,781,902,6002,581,541,800 1,314,484,935 1,308 509 MSI-H MSI-H MSI-H Cancer T7 TumorColorectal 50% 2,946,039,800 2,766,061,900 1,287,341,543 1,287 870 MSI-HMSI-H MSI-H Cancer T8 Tumor Colorectal 40% 3,418,941,400 3,141,032,2001,134,791,347 1,128 699 MSI-H MSI-H MSI-H Cancer T9 Tumor Colorectal 60%2,554,068,000 2,397,514,900 1,059,806,697 1,065 789 MSI-H MSI-H MSI-HCancer T10 Tumor Colorectal 70% 2,490,357,800 2,325,119,0001,041,133,466 1,045 577 MSI-H MSI-H MSI-H Cancer T11 Tumor Colorectal70% 2,802,989,000 2,574,326,700 1,021,889,116 1,028 611 MSI-H MSI-HMSI-H Cancer T12 Tumor Colorectal 60% 2,732,188,800 2,532,625,6001,102,256,555 1,106 809 MSS MSS MSS Cancer T13 Tumor Colorectal 60%3,374,700,400 3,160,846,000 1,444,383,958 1,452 856 MSS MSS MSS CancerT14 Tumor Colorectal 30% 4,449,316,000 4,158,857,900 1,912,478,277 1,9081,254 MSS MSS MSS Cancer T15 Tumor Colorectal 40% 3,221,878,6002,990,670,400 1,289,368,393 1,297 984 MSS MSS MSS Cancer T16 TumorColorectal 30% 2,706,508,600 2,523,131,100 1,106,624,877 1,112 859 MSSMSS MSS Cancer T17 Tumor Colorectal 25% 3,251,114,200 2,856,483,800961,918,119 966 736 MSS MSS MSS Cancer T18 Tumor Colorectal 30%3,231,913,800 3,009,768,900 1,360,021,648 1,348 991 MSS MSS MSS CancerT19 Tumor Colorectal 25% 3,363,038,600 3,113,118,000 1,384,620,931 1,376997 MSS MSS MSS Cancer T20 Tumor Colorectal 25% 2,438,680,6002,276,538,900 1,062,441,883 1,068 664 MSS MSS MSS Cancer T21 TumorColorectal 50% 3,835,047,200 3,616,937,100 1,599,268,481 1,585 1,070 MSSMSS MSS Cancer T22 Tumor Colorectal 50% 3,571,104,800 3,364,850,0001,560,304,548 1,549 1,027 MSS MSS MSS Cancer T23 Tumor Colorectal 50%3,358,858,000 3,148,152,000 1,543,361,455 1,531 381 MSS MSS MSS CancerT24 Tumor Colorectal 30% 3,800,714,600 3,454,271,300 1,451,100,665 1,4371,021 MSS MSS MSS Cancer T25 Tumor Colorectal 70% 2,786,623,6002,616,808,300 1,300,533,976 1,308 839 MSS MSS MSS Cancer T26 TumorColorectal 70% 2,745,441,000 2,560,749,200 1,144,854,802 1,150 831 MSSMSS MSS Cancer T27 Tumor Colorectal 50% 2,718,178,000 2,492,681,2001,007,856,362 1,009 772 MSS MSS MSS Cancer T28 Tumor Colorectal 50%3,811,856,800 3,469,104,100 1,178,261,647 1,164 881 MSS MSS MSS CancerT29 Tumor Colorectal 60% 2,836,284,200 2,639,980,200 1,070,525,027 1,076803 MSS MSS MSS Cancer T30 Tumor Colorectal 60% 3,054,498,8002,864,552,700 1,219,192,787 1,225 918 MSS MSS MSS Cancer T31 TumorColorectal 80% 2,580,688,000 2,400,044,300 974,909,901 980 778 MSS MSSMSS Cancer T32 Tumor Colorectal 25% 2,794,799,600 2,572,462,1001,100,684,869 1,106 852 MSS MSS MSS Cancer T33 Tumor Colorectal 25%4,145,168,800 3,782,601,800 1,600,830,943 1,581 1,023 MSS MSS MSS CancerT34 Tumor Colorectal 30% 2,805,656,200 2,574,761,000 1,165,019,629 1,164830 MSS MSS MSS Cancer T35 Tumor Colorectal 25% 3,314,533,6003,084,210,800 1,337,527,973 1,324 978 MSS MSS MSS Cancer T36 TumorColorectal 40% 3,083,111,800 2,855,861,400 1,234,493,152 1,237 871 MSSMSS MSS Cancer T37 Tumor Colorectal 50% 2,944,656,600 2,738,021,6001,185,096,762 1,184 871 MSI-H MSI-H MSI-H Cancer T38 Tumor Colorectal50% 2,753,927,200 2,556,267,100 1,111,967,617 1,107 826 MSI-H MSI-HMSI-H Cancer T39 Tumor Colorectal 50% 2,909,479,000 2,736,312,2001,240,864,480 1,245 880 MSI-H MSI-H MSI-H Cancer T40 Tumor Colorectal50% 2,861,106,600 2,664,312,500 1,238,921,489 1,235 816 MSI-H MSI-HMSI-H Cancer T41 Tumor Colorectal 50% 3,067,986,400 2,803,426,0001,187,719,134 1,191 807 MSI-H MSI-H MSI-H Cancer T42 Tumor Colorectal50% 2,575,126,400 2,352,723,700 985,780,477 986 729 MSI-H MSI-H MSI-HCancer T43 Tumor Colorectal 50% 3,553,245,200 3,291,761,0001,519,022,764 1,520 569 MSI-H MSI-H MSI-H Cancer T44 Tumor Colorectal50% 3,879,433,600 3,543,367,700 1,412,136,767 1,401 951 MSI-H MSI-HMSI-H Cancer T45 Tumor Colorectal 50% 2,906,836,200 2,640,548,9001,084,265,479 1,083 708 MSI-H MSI-H MSI-H Cancer T46 Tumor Colorectal50% 3,691,316,800 3,373,293,300 1,490,830,422 1,477 633 MSI-H MSI-HMSI-H Cancer T47 Tumor Colorectal 50% 3,682,074,800 3,431,016,5001,578,799,330 1,565 1,099 MSI-H MSI-H MSI-H Cancer T48 Tumor Colorectal50% 3,219,857,800 2,968,614,400 1,345,719,299 1,346 953 MSI-H MSI-HMSI-H Cancer T49 Tumor Colorectal 50% 3,682,200,200 3,314,391,2001,446,490,248 1,448 827 MSI-H MSI-H MSI-H Cancer T50 Tumor Colorectal50% 2,698,383,600 2,488,106,900 1,176,178,010 1,178 824 MSI-H MSI-HMSI-H Cancer T51 Tumor Colorectal 50% 3,319,692,800 2,956,351,2001,252,235,401 1,242 798 MSI-H MSI-H MSI-H Cancer T52 Tumor Colorectal50% 3,360,317,400 3,095,067,800 1,411,042,834 1,410 1,024 MSI-H MSI-HMSI-H Cancer T53 Tumor Colorectal 25% 2,961,310,600 2,731,451,4001,142,603,080 1,149 597 MSI-H MSI-H MSI-H Cancer T54 Tumor Colorectal25% 2,777,509,400 2,589,331,600 1,149,189,533 1,155 769 MSI-H MSI-HMSI-H Cancer T55 Tumor Colorectal 30% 2,639,198,800 2,442,326,100911,827,087 916 617 MSS MSS MSS Cancer T56 Tumor Colorectal 20%2,755,033,400 2,531,769,000 1,000,935,424 1,007 681 MSI-H MSI-H MSI-HCancer T57 Tumor Colorectal 20% 2,447,814,600 2,287,722,9001,015,055,726 1,020 696 MSS MSS MSS Cancer T58 Tumor Colorectal 20%3,286,578,200 3,057,086,600 1,307,312,335 1,314 961 MSS MSS MSS CancerT59 Tumor Colorectal 25% 2,903,957,600 2,684,504,100 1,230,635,449 1,238569 MSS MSS MSS Cancer T60 Tumor Colorectal 30% 3,057,011,0002,834,612,800 1,299,500,617 1,298 678 MSS MSS MSS Cancer T61 TumorColorectal 30% 3,524,443,800 3,332,286,300 1,616,082,336 1,617 890 MSI-HMSI-H MSI-H Cancer N1 Normal NA NA 1,544,823,000 1,450,418,000585,339,892 586 521 NA NA NA N2 Normal NA NA 1,902,151,400 1,773,538,400686,222,130 686 604 NA NA NA N3 Normal NA NA 1,845,286,600 1,717,939,600660,169,442 663 574 NA NA NA N4 Normal NA NA 1,747,777,400 1,604,382,200578,803,459 581 507 NA NA NA N5 Normal NA NA 1,358,892,200 1,270,257,700532,386,922 536 466 NA NA NA N6 Normal NA NA 1,403,909,400 1,328,105,100603,030,506 606 525 NA NA NA N7 Normal NA NA 1,477,544,600 1,386,426,800600,947,522 604 517 NA NA NA N8 Normal NA NA 1,922,041,400 1,784,316,400701,537,824 704 613 NA NA NA N9 Normal NA NA 1,389,792,400 1,302,753,500551,063,430 556 478 NA NA NA N10 Normal NA NA 1,368,669,2001,282,523,900 527,619,827 533 468 NA NA NA N11 Normal NA NA1,124,099,400 1,056,703,600 434,115,592 439 390 NA NA NA N12 Normal NANA 1,297,100,600 1,221,405,100 504,944,038 510 450 NA NA NA N13 NormalNA NA 1,320,243,600 1,222,723,200 477,732,672 482 362 NA NA NA N14Normal NA NA 2,096,304,800 1,924,550,100 629,989,948 634 563 NA NA NAN15 Normal NA NA 1,857,918,400 1,749,514,000 741,540,523 745 637 NA NANA N16 Normal NA NA 1,296,158,400 1,225,401,300 539,144,010 545 481 NANA NA N17 Normal NA NA 1,172,080,200 1,072,384,000 351,008,283 354 321NA NA NA N18 Normal NA NA 2,197,386,400 2,043,193,200 792,440,480 793683 NA NA NA N19 Normal NA NA 1,126,031,600 1,057,174,000 429,799,839435 388 NA NA NA N20 Normal NA NA 2,203,340,000 2,079,985,600923,434,168 927 780 NA NA NA N21 Normal NA NA 1,999,881,2001,849,375,700 663,902,271 666 578 NA NA NA N22 Normal NA NA1,919,525,200 1,795,389,600 721,484,943 723 621 NA NA NA N23 Normal NANA 1,331,809,200 1,260,705,600 596,587,867 602 517 NA NA NA N24 NormalNA NA 1,903,783,600 1,792,126,800 790,507,492 792 691 NA NA NA N25Normal NA NA 1,386,738,000 1,304,498,200 573,306,885 579 511 NA NA NAN26 Normal NA NA 1,288,502,200 1,211,465,700 518,972,645 524 465 NA NANA N27 Normal NA NA 2,531,366,000 2,385,834,400 1,019,543,903 1,027 883NA NA NA N28 Normal NA NA 1,992,785,000 1,873,292,800 802,670,846 804687 NA NA NA N29 Normal NA NA 1,455,104,600 1,366,776,900 575,386,950581 510 NA NA NA N30 Normal NA NA 1,664,936,200 1,561,641,100646,468,058 652 572 NA NA NA N31 Normal NA NA 1,200,639,8001,128,641,300 463,140,630 467 416 NA NA NA N32 Normal NA NA1,167,761,400 1,092,348,500 438,958,934 442 396 NA NA NA N33 Normal NANA 1,665,825,400 1,549,475,700 609,072,888 612 544 NA NA NA N34 NormalNA NA 1,367,489,200 1,290,133,900 556,470,787 563 499 NA NA NA N35Normal NA NA 1,503,631,400 1,412,540,500 580,358,931 585 509 NA NA NAN36 Normal NA NA 1,549,988,400 1,458,406,100 628,637,957 634 560 NA NANA N37 Normal NA NA 1,568,304,000 1,468,430,300 616,176,975 620 519 NANA NA N38 Normal NA NA 1,701,739,600 1,592,992,100 659,495,963 662 556NA NA NA N39 Normal NA NA 1,309,687,400 1,234,750,100 546,420,462 551464 NA NA NA N40 Normal NA NA 1,712,442,800 1,608,493,700 702,640,817705 588 NA NA NA N41 Normal NA NA 1,191,012,000 1,122,882,200485,771,595 491 415 NA NA NA N42 Normal NA NA 2,147,527,6002,012,745,300 865,817,634 870 721 NA NA NA N43 Normal NA NA2,632,733,800 2,480,587,000 1,109,697,317 1,120 878 NA NA NA N44 NormalNA NA 1,425,864,600 1,323,295,400 519,416,405 524 445 NA NA NA N45Normal NA NA 1,155,307,800 1,076,440,400 436,138,888 439 377 NA NA NAN46 Normal NA NA 1,221,955,800 1,150,302,700 518,176,709 523 444 NA NANA N47 Normal NA NA 1,869,437,000 1,735,921,400 728,621,647 732 602 NANA NA N48 Normal NA NA 1,587,657,000 1,487,987,900 631,794,049 637 537NA NA NA N49 Normal NA NA 1,781,366,200 1,673,812,500 729,333,555 734619 NA NA NA N50 Normal NA NA 1,148,277,400 1,076,124,300 342,181,739346 294 NA NA NA N51 Normal NA NA 1,904,405,800 1,764,410,700680,778,768 687 576 NA NA NA N52 Normal NA NA 1,640,495,2001,534,588,100 662,986,346 669 549 NA NA NA N53 Normal NA NA1,495,963,000 1,388,748,600 604,616,038 612 448 NA NA NA N54 Normal NANA 1,413,743,800 1,318,632,100 570,759,302 578 432 NA NA NA N55 NormalNA NA 1,254,353,600 1,146,429,600 439,784,552 445 346 NA NA NA N56Normal NA NA 1,358,890,600 1,256,367,000 500,055,838 506 375 NA NA NAN57 Normal NA NA 1,257,193,000 1,177,020,700 528,523,113 535 410 NA NANA N58 Normal NA NA 1,315,380,800 1,233,333,300 528,722,180 535 432 NANA NA N59 Normal NA NA 1,275,383,800 1,172,703,600 504,548,697 511 383NA NA NA N60 Normal NA NA 2,296,267,800 2,109,785,900 916,969,966 925645 NA NA NA N61 Normal NA NA 1,433,371,200 1,350,514,800 632,414,511639 475 NA NA NA

TABLE 3 Comparison of Microsatellite Status Determined through FFPETissue Analyses Promega MSI Analysis System FFPE Tissue Analysis MSI-HMSS 125 Gene MSI 31 0 Targeted Panel MSS 0 30

TABLE 4 58 Gene List for Plasma Analyses Gene (n = 58) Sequence RegionCovered AKT1 Hot Exon Analysis ALK Full RefSeq/CCDS Coding Sequence ARFull RefSeq/CCDS Coding Sequence ATM Hot Exon Analysis BRAF FullRefSeq/CCDS Coding Sequence BRCA1 Hot Exon Analysis BRCA2 Hot ExonAnalysis CCND1 Hot Exon Analysis CCND2 Hot Exon Analysis CCND3 Hot ExonAnalysis CD274 Full RefSeq/CCDS Coding Sequence CDK4 Full RefSeq/CCDSCoding Sequence CDK6 Full RefSeq/CCDS Coding Sequence CDKN2A Hot ExonAnalysis CTNNB1 Hot Exon Analysis DNMT3A Hot Exon Analysis EGFR FullRefSeq/CCDS Coding Sequence ERBB2 Full RefSeq/CCDS Coding Sequence ESR1Hot Exon Analysis EZH2 Hot Exon Analysis FGFR1 Hot Exon Analysis FGFR2Hot Exon Analysis FGFR3 Hot Exon Analysis FLT3 Hot Exon Analysis GNASHot Exon Analysis HRAS Hot Exon Analysis IDH1 Hot Exon Analysis IDH2 HotExon Analysis JAK2 Hot Exon Analysis KIT Full RefSeq/CCDS CodingSequence KRAS Full RefSeq/CCDS Coding Sequence MAP2K1 Kinase Domain METHot Exon Analysis + Adjacent Exon 14 Introns MTOR Hot Exon Analysis MYCHot Exon Analysis MYCN Hot Exon Analysis NPM1 Hot Exon Analysis NRAS HotExon Analysis NTRK1 Hot Exon Analysis NTRK2 Hot Exon Analysis NTRK3 HotExon Analysis PALB2 Hot Exon Analysis PIK3CA Hot Exon Analysis PIK3CBHot Exon Analysis PIK3R1 Hot Exon Analysis POLD1 Exonuclease Domain POLEExonuclease Domain PTCH1 Hot Exon Analysis PTEN Hot Exon Analysis RB1Hot Exon Analysis RET Full RefSeq/CCDS Coding Sequence RNF43 Hot ExonAnalysis ROS1 Kinase and Catalytic Domain TERT Hot Exon Analysis +Promoter TP53 Full RefSeq/CCDS Coding Sequence TSC1 Hot Exon AnalysisTSC2 Hot Exon Analysis VHL Hot Exon Analysis

TABLE 5 Summary of Next Generation Sequencing Statistics for HealthyDonor Samples, Contrived Samples, and Clinical Plasma Samples ClinicalBases Trial Mapped to Average Average Tissue Plasma Plasma BasesTargeted Total Distinct Enrollment MSI TMB Case Sample Bases Mapped toRegions of Coverage Coverage MSI Analysis Analysis ID Type SequencedGenome Interest (Fold) (Fold) Status Result Result HD1 Healthy4,610,602,000 4,591,372,800 2,313,780,895 23,135 1,326 NA MSS TMB- DonorLow HD2 Healthy 8,891,644,000 8,866,148,100 4,437,521,303 44,386 2,567NA MSS TMB- Donor Low HD3 Healthy 5,591,552,000 5,569,655,3002,532,932,984 25,273 1,413 NA MSS TMB- Donor Low HD4 Healthy5,573,545,400 5,543,102,100 2,255,758,545 22,481 1,654 NA MSS TMB- DonorLow HD5 Healthy 5,207,559,600 5,185,499,400 2,470,481,571 24,671 1,860NA MSS TMB- Donor Low HD6 Healthy 6,388,732,200 6,377,549,9003,432,543,524 34,319 3,762 NA MSS TMB- Donor Low HD7 Healthy4,734,677,000 4,712,020,800 2,345,085,749 23,450 1,514 NA MSS TMB- DonorLow HD8 Healthy 5,302,549,600 5,278,776,000 2,691,437,847 26,923 1,141NA MSS TMB- Donor Low HD9 Healthy 7,465,978,000 7,443,127,9003,937,377,476 39,278 2,632 NA MSS TMB- Donor Low HD10 Healthy6,074,039,400 6,052,256,300 3,176,126,200 31,723 1,707 NA MSS TMB- DonorLow HD11 Healthy 6,213,183,600 6,193,263,500 3,215,135,348 31,924 1,629NA MSS TMB- Donor Low HD12 Healthy 7,312,985,200 7,287,955,4003,361,626,922 33,392 2,219 NA MSS TMB- Donor Low HD13 Healthy6,510,483,400 6,494,893,800 2,976,435,079 29,539 4,803 NA MSS TMB- DonorLow HD14 Healthy 8,627,645,800 8,610,309,100 4,370,055,158 43,159 4,240NA MSS TMB- Donor Low HD15 Healthy 8,091,438,800 8,070,832,7004,064,773,281 40,137 2,755 NA MSS TMB- Donor Low HD16 Healthy8,479,048,000 8,460,878,600 4,274,823,876 42,218 2,387 NA MSS TMB- DonorLow HD17 Healthy 9,956,617,400 9,928,487,500 4,056,620,966 40,013 6,204NA MSS TMB- Donor Low HD18 Healthy 8,764,661,800 8,741,658,3004,365,489,881 43,257 1,659 NA MSS TMB- Donor Low HD19 Healthy7,889,783,000 7,869,480,500 3,564,990,160 35,371 3,264 NA MSS TMB- DonorLow HD20 Healthy 7,633,405,000 7,615,920,000 3,881,573,734 38,491 1,890NA MSS TMB- Donor Low HD21 Healthy 7,861,255,200 7,840,463,8003,898,962,179 38,636 1,558 NA MSS TMB- Donor Low HD22 Healthy4,781,596,200 4,700,767,800 2,047,246,059 20,023 914 NA MSS TMB- DonorLow HD23 Healthy 6,681,047,200 6,637,094,200 3,496,324,530 34,651 1,777NA MSS TMB- Donor Low HD24 Healthy 7,177,461,600 7,153,542,9003,634,434,110 36,048 1,926 NA MSS TMB- Donor Low HD25 Healthy7,434,671,400 7,407,050,700 3,898,804,784 38,653 2,302 NA MSS TMB- DonorLow HD26 Healthy 7,429,101,000 7,401,652,100 3,673,202,567 36,392 2,038NA MSS TMB- Donor Low HD27 Healthy 8,503,220,200 8,481,220,9004,481,836,913 44,189 4,007 NA MSS TMB- Donor Low HD28 Healthy7,913,436,400 7,891,591,400 3,999,331,489 39,604 6,476 NA MSS TMB- DonorLow HD29 Healthy 4,614,537,000 4,554,941,500 2,105,579,210 20,597 697 NAMSS TMB- Donor Low HD30 Healthy 7,492,256,600 7,465,117,2003,476,188,532 34,328 2,857 NA MSS TMB- Donor Low HD31 Healthy8,328,282,600 8,286,892,200 4,210,419,884 41,650 3,095 NA MSS TMB- DonorLow HD32 Healthy 7,016,633,400 6,995,998,500 3,531,038,365 34,933 1,236NA MSS TMB- Donor Low HD33 Healthy 8,194,639,600 8,172,001,6004,176,117,225 41,166 2,952 NA MSS TMB- Donor Low HD34 Healthy6,007,170,600 5,988,709,000 2,841,711,258 28,277 3,526 NA MSS TMB- DonorLow HD35 Healthy 7,712,474,800 7,687,926,200 3,538,858,830 34,870 3,962NA MSS TMB- Donor Low HD36 Healthy 6,447,382,600 6,427,425,7003,393,662,901 33,415 2,859 NA MSS TMB- Donor Low HD37 Healthy8,134,672,200 8,105,317,200 4,054,212,131 39,967 1,544 NA MSS TMB- DonorLow HD38 Healthy 5,535,483,200 5,524,427,300 2,816,615,357 28,054 1,983NA MSS TMB- Donor Low HD39 Healthy 7,564,324,200 7,546,630,9003,764,230,400 37,490 4,300 NA MSS TMB- Donor Low HD40 Healthy8,036,286,000 8,014,954,300 4,048,484,998 40,197 3,096 NA MSS TMB- DonorLow HD41 Healthy 7,640,735,400 7,622,537,500 3,929,173,586 39,049 1,971NA MSS TMB- Donor Low HD42 Healthy 6,677,376,600 6,656,214,2002,797,826,119 27,836 1,938 NA MSS TMB- Donor Low HD43 Healthy8,409,420,800 8,391,690,200 4,451,721,807 43,978 3,316 NA MSS TMB- DonorLow HD44 Healthy 8,467,700,000 8,440,226,300 3,675,196,602 36,497 5,083NA MSS TMB- Donor Low HD45 Healthy 7,197,353,200 7,170,267,7003,698,926,248 36,497 1,831 NA MSS TMB- Donor Low HD46 Healthy8,318,236,800 8,281,776,900 4,148,545,120 40,815 1,773 NA MSS TMB- DonorLow HD47 Healthy 9,006,412,400 8,978,934,000 4,760,007,319 46,907 2,891NA MSS TMB- Donor Low HD48 Healthy 7,344,659,400 7,321,998,7003,843,864,822 37,828 1,883 NA MSS TMB- Donor Low HD49 Healthy8,288,914,400 8,270,435,900 4,445,831,613 43,940 3,272 NA MSS TMB- DonorLow HD50 Healthy 8,639,110,000 8,615,224,600 4,502,933,702 44,423 2,244NA MSS TMB- Donor Low HD51 Healthy 7,575,511,200 7,555,273,9003,939,210,286 39,053 3,320 NA MSS TMB- Donor Low HD52 Healthy8,427,667,800 8,400,593,500 4,420,970,865 43,296 3,497 NA MSS TMB- DonorLow HD53 Healthy 8,542,647,000 8,516,087,000 4,385,330,122 42,944 3,771NA MSS TMB- Donor Low HD54 Healthy 8,453,014,000 8,428,500,7004,387,819,781 43,296 2,325 NA MSS TMB- Donor Low HD55 Healthy9,298,955,400 9,271,088,500 4,819,496,438 47,546 2,341 NA MSS TMB- DonorLow HD56 Healthy 8,478,268,200 8,444,312,700 4,094,638,378 40,360 2,050NA MSS TMB- Donor Low HD57 Healthy 8,199,783,400 8,170,058,6003,957,778,287 39,001 2,457 NA MSS TMB- Donor Low HD58 Healthy9,346,566,000 9,314,731,900 4,924,333,229 48,509 1,727 NA MSS TMB- DonorLow HD59 Healthy 8,919,385,800 8,892,662,500 4,390,523,968 43,293 3,513NA MSS TMB- Donor Low HD60 Healthy 8,389,446,000 8,370,187,1004,369,738,805 43,148 1,477 NA MSS TMB- Donor Low HD61 Healthy9,905,663,600 9,881,313,200 4,974,727,427 49,048 6,168 NA MSS TMB- DonorLow HD62 Healthy 9,174,224,000 9,148,992,500 4,535,168,624 44,655 2,138NA MSS TMB- Donor Low HD63 Healthy 8,084,463,600 8,057,658,8003,968,831,440 38,915 1,992 NA MSS TMB- Donor Low HD64 Healthy8,983,082,400 8,950,678,700 3,826,555,016 37,464 3,223 NA MSS TMB- DonorLow HD65 Healthy 7,442,509,800 7,422,697,300 3,854,553,775 38,261 2,341NA MSS TMB- Donor Low HD66 Healthy 8,337,674,200 8,316,432,9004,007,211,501 39,529 2,369 NA MSS TMB- Donor Low HD67 Healthy7,154,104,000 7,129,207,200 3,440,461,741 34,040 2,871 NA MSS TMB- DonorLow HD68 Healthy 8,659,740,200 8,618,184,400 4,184,609,095 41,321 3,957NA MSS TMB- Donor Low HD69 Healthy 7,771,232,400 7,753,568,6003,681,096,130 36,485 3,124 NA MSS TMB- Donor Low HD70 Healthy4,405,077,200 4,384,751,000 1,552,688,622 15,295 2,094 NA MSS TMB- DonorLow HD71 Healthy 5,920,713,000 5,898,405,200 2,633,560,458 26,073 1,061NA MSS TMB- Donor Low HD72 Healthy 7,579,429,200 7,554,654,8002,748,860,562 27,222 2,043 NA MSS TMB- Donor Low HD73 Healthy8,631,626,800 8,607,231,300 3,343,564,755 33,121 3,291 NA MSS TMB- DonorLow HD74 Healthy 6,949,033,000 6,931,273,900 3,127,174,082 30,958 3,031NA MSS TMB- Donor Low HD75 Healthy 5,875,099,600 5,864,389,1002,962,088,150 29,390 4,277 NA MSS TMB- Donor Low HD76 Healthy6,626,185,400 6,609,772,800 3,084,072,011 30,517 2,136 NA MSS TMB- DonorLow HD77 Healthy 11,291,302,400 11,238,394,500 5,110,554,826 49,9233,453 NA MSS TMB- Donor Low HD78 Healthy 5,515,433,800 5,483,434,1001,965,775,908 19,120 834 NA MSS TMB- Donor Low HD79 Healthy6,954,396,400 6,931,242,800 3,311,490,206 32,327 3,145 NA MSS TMB- DonorLow HD80 Healthy 6,152,936,200 6,131,263,700 2,720,849,245 26,546 2,270NA MSS TMB- Donor Low HD81 Healthy 8,733,434,600 8,702,900,4004,271,410,537 41,795 3,890 NA MSS TMB- Donor Low HD82 Healthy6,720,050,800 6,692,163,400 2,871,213,549 28,127 2,200 NA MSS TMB- DonorLow HD83 Healthy 7,729,687,400 7,705,631,600 3,769,457,577 37,031 3,098NA MSS TMB- Donor Low HD84 Healthy 8,665,550,000 8,633,041,4004,135,285,473 40,550 2,542 NA MSS TMB- Donor Low HD85 Healthy7,972,481,400 7,950,462,100 3,776,002,282 37,290 3,000 NA MSS TMB- DonorLow HD86 Healthy 8,250,349,800 8,215,560,400 4,149,026,011 40,906 2,274NA MSS TMB- Donor Low HD87 Healthy 7,218,789,600 7,194,779,1003,266,906,096 32,493 3,137 NA MSS TMB- Donor Low HD88 Healthy6,682,720,200 6,654,240,600 3,392,475,194 33,738 2,498 NA MSS TMB- DonorLow HD89 Healthy 6,871,691,000 6,856,541,800 3,521,282,340 34,894 2,744NA MSS TMB- Donor Low HD90 Healthy 8,772,448,000 8,749,280,6004,178,273,953 41,258 1,494 NA MSS TMB- Donor Low HD91 Healthy7,480,832,800 7,457,217,500 3,595,805,873 35,471 2,266 NA MSS TMB- DonorLow HD92 Healthy 5,975,083,600 5,958,618,100 2,873,989,002 28,615 1,701NA MSS TMB- Donor Low HD93 Healthy 5,375,821,400 5,360,555,5002,567,619,902 25,583 2,090 NA MSS TMB- Donor Low HD94 Healthy6,280,445,200 6,260,287,600 3,139,767,399 31,191 2,533 NA MSS TMB- DonorLow HD95 Healthy 8,135,958,600 8,115,624,700 4,130,225,448 40,731 2,317NA MSS TMB- Donor Low HD96 Healthy 7,017,152,200 7,000,775,9003,355,455,453 33,091 2,169 NA MSS TMB- Donor Low HD97 Healthy7,423,045,000 7,401,835,700 3,612,561,174 35,619 1,489 NA MSS TMB- DonorLow HD98 Healthy 7,575,649,400 7,542,405,300 3,306,732,212 32,637 1,985NA MSS TMB- Donor Low HD99 Healthy 8,101,683,000 8,073,383,3003,916,838,207 38,584 2,250 NA MSS TMB- Donor Low HD100 Healthy8,227,634,200 8,195,376,800 3,707,557,242 36,571 1,908 NA MSS TMB- DonorLow HD101 Healthy 7,409,985,800 7,378,039,500 2,943,037,267 29,002 1,815NA MSS TMB- Donor Low HD102 Healthy 7,813,906,600 7,786,978,9003,615,038,253 35,836 3,086 NA MSS TMB- Donor Low HD103 Healthy7,127,926,200 7,092,785,200 2,681,989,940 26,428 1,720 NA MSS TMB- DonorLow HD104 Healthy 7,010,324,000 6,980,650,200 2,688,769,169 26,520 1,612NA MSS TMB- Donor Low HD105 Healthy 7,822,779,200 7,785,449,9003,212,888,783 31,319 1,715 NA MSS TMB- Donor Low HD106 Healthy7,364,897,200 7,339,416,100 3,586,900,299 35,320 2,452 NA MSS TMB- DonorLow HD107 Healthy 8,493,402,800 8,458,319,500 3,260,053,476 32,076 3,556NA MSS TMB- Donor Low HD108 Healthy 9,834,233,000 9,805,201,2004,706,975,042 46,015 2,353 NA MSS TMB- Donor Low HD109 Healthy6,747,679,000 6,733,999,600 3,110,916,970 30,972 1,938 NA MSS TMB- DonorLow HD110 Healthy 7,069,059,200 7,054,426,600 3,473,558,000 34,223 2,862NA MSS TMB- Donor Low HD111 Healthy 10,374,032,800 10,334,762,5005,226,977,407 51,127 3,737 NA MSS TMB- Donor Low HD112 Healthy9,373,668,400 9,330,427,800 3,826,086,189 37,392 2,960 NA MSS TMB- DonorLow HD113 Healthy 6,510,073,600 6,495,529,700 3,083,434,125 30,646 2,714NA MSS TMB- Donor Low HD114 Healthy 5,788,275,000 5,775,686,7002,790,114,913 27,752 1,891 NA MSS TMB- Donor Low HD115 Healthy5,628,781,800 5,608,788,400 2,324,130,331 23,040 1,336 NA MSS TMB- DonorLow HD116 Healthy 6,622,736,800 6,595,711,900 2,853,684,669 28,019 1,732NA MSS TMB- Donor Low HD117 Healthy 8,235,416,200 8,206,562,8004,066,198,888 40,037 2,147 NA MSS TMB- Donor Low HD118 Healthy8,142,539,800 8,113,673,000 3,498,426,122 34,518 2,319 NA MSS TMB- DonorLow HD119 Healthy 6,567,610,600 6,552,480,100 3,520,404,897 35,102 1,423NA MSS TMB- Donor Low HD120 Healthy 8,172,503,000 8,146,438,0003,738,973,301 36,834 2,391 NA MSS TMB- Donor Low HD121 Healthy7,086,855,800 7,066,717,300 3,531,225,022 35,183 2,010 NA MSS TMB- DonorLow HD122 Healthy 6,632,081,800 6,613,761,500 2,824,890,605 28,087 4,498NA MSS TMB- Donor Low HD123 Healthy 8,716,718,200 8,692,336,5004,249,954,641 42,108 2,897 NA MSS TMB- Donor Low HD124 Healthy5,846,065,600 5,827,023,000 2,398,968,620 23,846 1,985 NA MSS TMB- DonorLow HD125 Healthy 5,987,677,000 5,975,740,400 3,037,726,557 30,200 1,896NA MSS TMB- Donor Low HD126 Healthy 6,450,910,600 6,433,946,8002,901,586,776 28,839 1,732 NA MSS TMB- Donor Low HD127 Healthy6,521,277,600 6,505,071,200 3,237,555,270 32,254 1,946 NA MSS TMB- DonorLow HD128 Healthy 5,183,805,800 5,174,096,900 2,624,001,866 26,114 1,626NA MSS TMB- Donor Low HD129 Healthy 6,060,923,800 6,032,344,9003,294,503,839 33,028 2,041 NA MSS TMB- Donor Low HD130 Healthy6,931,215,400 6,664,206,100 2,861,090,396 27,128 2,616 NA MSS TMB- DonorLow HD131 Healthy 6,881,530,800 6,868,642,100 3,207,081,669 31,800 5,415NA MSS TMB- Donor Low HD132 Healthy 8,447,741,200 8,422,297,1004,171,378,734 41,085 2,511 NA MSS TMB- Donor Low HD133 Healthy6,647,519,000 6,618,288,100 3,015,315,572 29,699 974 NA MSI TMB- DonorLow HD134 Healthy 9,017,435,200 8,992,906,300 4,677,984,882 46,146 3,595NA MSS TMB- Donor Low HD135 Healthy 6,184,647,200 6,158,774,7002,847,813,354 28,060 1,659 NA MSS TMB- Donor Low HD136 Healthy7,819,615,400 7,794,596,100 3,895,863,689 38,457 2,214 NA MSS TMB- DonorLow HD137 Healthy 9,297,185,200 9,266,997,400 4,986,929,926 49,160 3,316NA MSS TMB- Donor Low HD138 Healthy 6,088,725,600 6,071,004,4002,871,318,994 28,468 1,376 NA MSS TMB- Donor Low HD139 Healthy7,078,148,600 7,064,739,600 3,681,471,294 36,397 3,204 NA MSS TMB- DonorLow HD140 Healthy 7,991,284,600 7,973,783,500 3,998,658,283 39,667 3,312NA MSS TMB- Donor Low HD141 Healthy 8,078,032,000 8,054,876,8004,060,189,135 40,087 1,762 NA MSS TMB- Donor Low HD142 Healthy7,768,653,400 7,744,059,500 3,415,300,515 33,884 1,990 NA MSS TMB- DonorLow HD143 Healthy 6,099,199,600 6,080,885,000 2,775,841,946 27,543 3,106NA MSS TMB- Donor Low HD144 Healthy 7,710,555,200 7,694,753,1003,714,118,122 36,768 3,794 NA MSS TMB- Donor Low HD145 Healthy7,799,141,400 7,769,239,200 3,941,900,921 38,944 2,524 NA MSS TMB- DonorLow HD146 Healthy 6,726,282,600 6,712,047,100 3,413,345,451 33,896 2,071NA MSS TMB- Donor Low HD147 Healthy 7,976,941,800 7,958,488,7003,953,916,025 39,148 3,075 NA MSS TMB- Donor Low HD148 Healthy6,773,777,600 6,756,376,400 3,398,770,425 33,685 1,878 NA MSS TMB- DonorLow HD149 Healthy 7,241,584,800 7,214,148,600 3,197,797,413 31,671 1,957NA MSS TMB- Donor Low HD150 Healthy 8,772,019,000 8,744,087,4004,198,896,205 41,505 2,404 NA MSS TMB- Donor Low HD151 Healthy9,597,923,600 9,554,309,500 4,304,212,463 42,013 1,918 NA MSS TMB- DonorLow HD152 Healthy 9,766,675,200 9,730,121,800 4,232,551,267 41,381 2,131NA MSS TMB- Donor Low HD153 Healthy 7,964,424,400 7,902,830,7003,958,568,012 38,872 2,430 NA MSS TMB- Donor Low HD154 Healthy8,703,468,000 8,679,744,500 4,526,278,703 44,722 5,251 NA MSS TMB- DonorLow HD155 Healthy 7,877,226,800 7,859,052,900 3,985,810,986 39,418 3,773NA MSS TMB- Donor Low HD156 Healthy 7,747,095,200 7,729,059,8003,978,272,416 39,396 3,948 NA MSS TMB- Donor Low HD157 Healthy7,689,538,200 7,670,324,700 3,678,385,491 36,403 2,146 NA MSS TMB- DonorLow HD158 Healthy 6,036,060,400 6,021,050,700 2,871,615,508 28,452 1,793NA MSS TMB- Donor Low HD159 Healthy 9,284,331,400 9,261,722,3004,572,013,112 45,219 4,489 NA MSS TMB- Donor Low HD160 Healthy8,039,083,200 8,017,829,900 3,668,403,298 36,433 2,958 NA MSS TMB- DonorLow HD161 Healthy 6,337,931,000 6,315,879,600 2,863,889,709 28,364 1,286NA MSS TMB- Donor Low HD162 Healthy 10,292,765,800 10,260,537,4005,291,273,703 51,768 3,546 NA MSS TMB- Donor Low HD163 Healthy9,258,149,800 9,233,715,900 4,485,768,075 43,995 4,143 NA MSS TMB- DonorLow CL1 LS180 7,612,745,000 7,589,215,100 3,392,459,288 33,523 2,083 MSIMSI N/A CL2 LS411N 7,678,713,000 7,654,819,800 3,291,800,936 32,5322,149 MSI MSI N/A CL3 SNU- 6,256,132,400 6,240,909,800 2,807,306,20727,761 2,420 MSI MSI N/A C2B CL4 RKO 7,066,840,000 7,048,897,5003,177,373,078 31,421 2,085 MSI MSS N/A CL5 SNU- 7,669,517,6007,650,812,200 3,439,833,485 34,079 3,069 MSI MSI N/A C2A CL6 LS1808,691,502,000 8,658,803,000 3,445,624,572 33,838 2,426 MSI MSI N/A CL7LS180 8,535,101,200 8,503,984,000 3,893,865,285 38,211 2,595 MSI MSI N/ACL8 LS180 8,083,764,400 8,056,986,800 3,780,724,828 37,152 2,455 MSI MSIN/A CL9 LS180 7,904,478,600 7,881,702,700 3,696,511,241 36,324 2,407 MSIMSI N/A CLIO LS180 7,764,828,000 7,737,044,900 3,531,063,394 34,4552,138 MSI MSI N/A CL11 LS411N 8,245,419,000 8,222,207,200 3,748,315,49236,967 2,471 MSI MSI N/A CL12 LS411N 6,575,842,800 6,554,550,7003,030,898,415 29,795 2,430 MSI MSS N/A CL13 LS411N 8,271,559,0008,245,273,600 3,762,761,032 36,919 2,295 MSI MSI N/A CL14 LS411N7,934,153,000 7,905,178,000 3,458,080,463 33,948 2,451 MSI MSI N/A CL15LS411N 7,108,328,800 7,085,747,100 3,057,622,227 30,157 2,159 MSI MSIN/A CL16 SNU- 8,456,505,800 8,424,591,600 3,925,699,391 38,462 2,482 MSIMSI N/A C2B CL17 SNU- 7,577,529,000 7,556,499,800 3,380,433,809 33,4242,261 MSI MSI N/A C2B CL18 SNU- 6,993,859,200 6,976,543,6003,225,795,617 31,918 2,171 MSI MSI N/A C2B CL19 SNU- 5,882,123,6005,860,372,800 2,447,923,970 24,221 2,066 MSI MSI N/A C2B CL20 SNU-7,878,616,400 7,858,594,100 3,506,238,369 34,685 2,058 MSI MSI N/A C2BCS94P1 Clinical 9,263,762,400 9,244,770,800 3,825,312,992 37,868 8,416MSI MSI TMB- Low CS94P2 Clinical 8,813,423,000 8,792,480,6003,978,488,566 39,021 8,506 Timepoint MSI N/A Sample CS94P3 Clinical8,964,792,200 8,937,833,100 3,676,739,247 36,159 9,963 Timepoint MSS N/ASample CS95P1 Clinical 7,636,898,200 7,570,902,200 2,114,468,194 20,8042,175 MSI MSS TMB- Low CS95P2 Clinical 8,719,884,400 8,686,639,3003,776,909,959 37,371 3,279 Timepoint MSS N/A Sample CS95P3 Clinical7,946,606,600 7,923,725,300 3,681,799,356 36,417 3,069 Timepoint MSI N/ASample CS96P1 Clinical 8,340,755,000 8,311,711,700 3,710,084,604 36,6905,686 MSS MSS TMB- Low CS96P2 Clinical 6,198,454,200 6,168,225,1002,565,799,692 24,735 5,781 Timepoint MSS N/A Sample CS96P3 Clinical5,912,813,200 5,893,031,300 2,980,746,401 28,844 7,161 Timepoint MSS N/ASample CS97P1 Clinical 7,017,701,200 6,998,604,600 3,287,599,575 31,8398,100 MSI MSI TMB- High CS97P2 Clinical 7,308,707,000 7,285,576,0003,660,108,635 35,033 8,563 Timepoint MSI N/A Sample CS97P3 Clinical5,469,610,600 5,445,096,000 2,704,635,549 25,902 4,807 Timepoint MSS N/ASample CS97P4 Clinical 6,624,844,800 6,602,615,600 3,295,259,498 31,7525,692 Timepoint MSS N/A Sample CS97P5 Clinical 7,934,394,4007,916,551,300 3,787,601,674 36,642 7,400 Timepoint MSS N/A Sample CS97P6Clinical 5,527,711,600 5,504,889,500 2,466,586,812 23,568 3,104Timepoint MSS N/A Sample CS98P1 Clinical 6,412,760,400 6,389,582,1003,056,873,694 29,246 6,535 MSI MSI TMB- High CS98P2 Clinical6,672,529,200 6,656,140,900 3,244,885,418 31,287 6,232 Timepoint MSI N/ASample CS98P3 Clinical 7,239,611,200 7,210,354,900 3,337,128,346 31,9604,571 Timepoint MSS N/A Sample CS98P4 Clinical 4,884,469,6004,870,410,300 2,398,774,907 23,146 3,886 Timepoint MSS N/A Sample CS98P5Clinical 6,684,455,800 6,629,107,600 3,043,981,443 29,758 2,048Timepoint MSS N/A Sample CS99P1 Clinical 7,515,207,800 7,492,829,5003,558,158,353 35,107 3,567 MSS MSS TMB- Low CS99P2 Clinical7,295,781,200 7,266,983,100 3,137,279,510 30,900 2,698 Timepoint MSS N/ASample CS99P3 Clinical 8,069,010,600 8,015,635,800 3,679,851,982 36,0474,776 Timepoint MSS N/A Sample CS99P4 Clinical 7,293,700,4007,259,226,900 3,264,415,728 32,140 2,399 Timepoint MSS N/A Sample CS00P1Clinical 6,374,270,600 6,354,057,400 3,037,772,591 29,333 4,464 MSI MSITMB- High CS00P2 Clinical 7,800,574,000 7,772,352,900 3,639,270,01335,789 7,769 Timepoint MSS N/A Sample CS00P3 Clinical 8,999,308,8008,975,347,800 4,386,806,111 43,097 7,970 Timepoint MSS N/A Sample CS00P4Clinical 8,380,704,400 8,356,079,400 4,080,252,921 40,115 6,470Timepoint MSS N/A Sample CS00P5 Clinical 9,582,201,400 9,546,328,1003,353,260,614 32,916 6,017 Timepoint MSS N/A Sample CS00P6 Clinical10,156,844,200 10,115,758,100 4,837,193,865 47,487 3,644 Timepoint MSSN/A Sample CS01P1 Clinical 8,967,808,600 8,936,498,200 2,764,042,29627,189 7,682 MSS MSS TMB- Low CS01P2 Clinical 7,912,113,0007,890,663,500 4,022,370,530 38,855 8,822 Timepoint MSS N/A Sample CS01P3Clinical 6,484,354,600 6,455,565,800 3,188,722,876 30,729 7,517Timepoint MSS N/A Sample CS02P1 Clinical 4,189,797,200 4,152,277,3001,904,218,928 18,244 1,223 MSI MSS TMB- Low CS02P2 Clinical10,780,428,800 10,746,068,700 5,446,642,208 52,607 4,747 Timepoint MSSN/A Sample CS03P1 Clinical 7,050,276,200 7,025,996,100 3,272,196,91431,585 4,411 MSI MSI TMB- High CS03P2 Clinical 7,863,350,8007,834,129,600 3,881,768,067 37,547 4,004 Timepoint MSS N/A Sample CS03P3Clinical 5,886,551,400 5,855,839,700 2,592,954,330 25,054 1,821Timepoint MSS N/A Sample CS03P4 Clinical 5,120,290,200 5,089,916,8002,462,851,445 23,841 1,370 Timepoint MSS N/A Sample CS04P1 Clinical7,761,417,200 7,737,522,100 3,680,626,639 35,734 5,451 MSS MSS TMB- LowCS04P2 Clinical 7,248,720,000 7,230,958,400 3,711,116,296 36,019 5,988Timepoint MSS N/A Sample CS04P3 Clinical 6,981,545,200 6,963,312,6003,392,271,571 32,934 6,670 Timepoint MSS N/A Sample CS04P4 Clinical8,074,351,200 8,052,943,600 3,852,185,218 37,331 8,083 Timepoint MSS N/ASample CS04P5 Clinical 5,970,210,800 5,949,218,300 2,795,371,329 27,0478,641 Timepoint MSS N/A Sample CS05P1 Clinical 5,968,039,8005,946,488,700 2,729,734,351 26,410 5,574 MSI MSI TMB- Low CS05P2Clinical 6,623,933,000 6,600,539,800 3,226,910,099 31,277 6,687Timepoint MSI N/A Sample CS05P3 Clinical 4,496,120,400 4,477,585,5002,194,338,785 21,245 3,297 Timepoint MSI N/A Sample CS06P1 Clinical8,211,159,400 8,186,988,800 4,156,128,019 40,389 8,002 MSI MSI TMB- HighCS06P2 Clinical 6,178,650,200 6,150,639,400 2,894,449,740 27,945 6,038Timepoint MSI N/A Sample CS06P3 Clinical 6,478,543,800 6,455,283,9003,163,299,171 30,602 4,990 Timepoint MSS N/A Sample CS06P4 Clinical6,548,847,200 6,526,980,300 3,098,470,879 30,056 6,184 Timepoint MSI N/ASample CS06P5 Clinical 5,595,054,000 5,528,052,700 2,505,771,405 23,6843,530 Timepoint MSI N/A Sample CS07P1 Clinical 10,952,067,60010,913,583,200 2,792,942,847 27,492 8,179 MSI MSI TMB- High CS07P2Clinical 10,529,570,200 10,492,696,000 4,154,390,112 40,862 7,298Timepoint MSI N/A Sample CS07P3 Clinical 9,716,580,000 9,688,288,1004,626,575,627 45,358 9,111 Timepoint MSI N/A Sample CS08P1 Clinical6,015,494,400 5,979,103,500 2,862,781,196 27,006 7,909 MSI MSI TMB- LowCS08P2 Clinical 6,132,402,200 6,089,687,400 3,043,241,991 28,664 9,537Timepoint MSI N/A Sample CS08P3 Clinical 6,909,139,800 6,867,393,1003,360,042,873 31,839 7,216 Timepoint MSI N/A Sample CS09P1 Clinical5,711,066,800 5,673,140,500 2,598,635,892 24,508 4,781 MSI MSS TMB- LowCS09P2 Clinical 6,038,788,400 6,017,962,600 2,867,990,049 27,725 6,346Timepoint MSS N/A Sample

TABLE 6 Comparison of Microsatellite Status Determined through HealthyDonor, Contrived, and Clinical Plasma Analyses Healthy Donors andExpected Status Contrived Sample Analysis MSI-H MSS 58 Gene TargetedPanel MSI 18 1 MSS 2 162 Tissue MSI Status Clinical Plasma AnalysisMSI-H MSS 58 Gene Targeted Panel MSI 9 0 MSS 3 4

TABLE 7 Summary of Clinical Information for 16 Patients Evaluated forResponse to Immune Checkpoint Blockade Tissue Lynch Time EnrollmentMetastases Syndrome to Best Time Time Duration of Case MSI Tumor StageDetected At (Medical Best Response to ORR to CR Response ID Status Type(On Study) Baseline History) Reponse (Months) (Months) (Months) (Months)CS94 MSI-H Ampulla of IV Y Lynch PD N/A N/A N/A N/A Vater syndrome CS95MSI-H Small IV Y Lynch PD N/A N/A N/A N/A Intestine syndrome CS96 MSSColorectal IV Y No PD N/A N/A N/A N/A CS97 MSI-H Colorectal IV Y LynchCR 20.2 6.5 20.2 34.7 syndrome CS98 MSI-H Colorectal IV Y Lynch CR 16.912.4  16.9 36.2 syndrome CS99 MSS Colorectal IV Y No PD N/A N/A N/A N/ACS00 MSI-H Ampulla of IV Y Lynch CR 17.1 2.4 17.0 45.4 Vater syndromeCS01 MSS Colorectal IV Y No PD N/A N/A N/A N/A CS02 MSI-H Small IV Y NoPR  2.6 2.6 N/A  4.8 Intestine CS03 MSI-H Colorectal IV Y Lynch CR 15.22.9 15.2 39.1 syndrome CS04 MSS Colorectal IV Y No PD N/A N/A N/A N/ACS05 MSI-H Colorectal IV Y Lynch PD N/A N/A N/A N/A syndrome CS06 MSI-HColorectal IV Y Lynch PR  2.6 2.6 N/A 13.6 syndrome CS07 MSI-H Ampullaof IV Y Lynch NE N/A N/A N/A N/A Vater syndrome CS08 MSI-H Colorectal IVY No PD N/A N/A N/A N/A CS09 MSI-H Colorectal IV Y Unknown PD N/A N/AN/A N/A Two Consecutive Timepoints with >80% Reduction in ProgressionBaseline Free Overall Last Protein Protein Case Survival Survival DoseCensored Censored Biomarkers Biomarker ID (Months) (Months) (Months)(Progression) (Overall) Evaluated Levels CS94 3.0 3.6 3.0 1 1 CEA NoCS95 2.8 20.7 5.6 1 1 CEA N/A - Baseline Normal Reference Range CS96 2.85.0 2.4 1 1 CEA No CS97 41.2 48.8 10.6 0 0 CEA Yes CS98 48.6 48.8 23.8 00 CEA Yes CS99 2.8 8.8 2.3 1 1 CEA No CS00 47.8 47.8 23.9 0 0 CEA; CEA:N/A - Baseline Normal CA19-9 Reference Range CA19-9: Yes CS01 1.7 4.91.8 1 1 CEA No CS02 5.5 43.9 23.6 1 0 CEA; CEA: N/A - Baseline NormalCA19-9 Reference Range CA19-9: N/A - Baseline Normal Reference RangeCS03 42.0 42.0 23.8 0 0 CEA N/A - Baseline Normal Reference Range CS042.9 7.6 3.8 1 1 CEA No CS05 2.9 15.9 4.8 1 1 CEA No CS06 16.2 40.0 23.71 0 CEA No CS07 2.4 2.4 1.4 1 1 CEA No CS08 3.0 7.6 3.4 1 1 CEA N/A -Baseline Normal Reference Range CS09 1.4 6.9 4.5 1 1 CEA No Plasma ExomeMutation Total Plasma Plasma Plasma Plasma Plasma Plasma Mutation LoadPlasma Time Time Time Time Time Time Load (mutations/ Case Samples Point1 Point 2 Point 3 Point 4 Point 5 Point 6 (mutations/ Mbp ID Evaluated(Months) (Months) (Months) (Months) (Months) (Months) Mbp) Sequenced)CS94 3 0.1 0.5 3.0 N/A N/A N/A 23.1 40.6 CS95 3 0.0 0.5 6.5 N/A N/A N/A64.0 10.2 CS96 3 0.0 0.5 2.8 N/A N/A N/A 0.1 10.2 CS97 6 0.0 0.5 2.810.6 12.5 22.8 70.2 111.7 CS98 5 0.0 0.5 4.8 14.0 28.7 N/A 120.5 203.2CS99 4 0.0 0.5 0.9  2.8 N/A N/A N/A 10.2 CS00 6 0.0 0.6 2.9  4.4 11.725.9 139.2 152.4 CS01 3 0.0 0.5 0.9 N/A N/A N/A 2.3 20.3 CS02 2 0.0 0.6N/A N/A N/A N/A 40.8 0.0 CS03 4 0.0 0.6 12.8  27.3 N/A N/A 28.2 50.8CS04 5 0.0 0.6 1.3  2.9  4.5 N/A 0.8 20.3 CS05 3 0.0 0.5 4.8 N/A N/A N/A39.8 10.2 CS06 5 0.0 0.5 5.1 11.1 23.7 N/A 11.0 91.4 CS07 3 0.0 0.4 0.9N/A N/A N/A 68.7 233.6 CS08 3 0.0 0.7 3.0 N/A N/A N/A N/A 40.6 CS09 20.0 0.7 N/A N/A N/A N/A N/A 20.3 Two Two Time Consecutive ConsecutiveDifference Baseline Timepoints Timepoints Between Plasma with >90% with0% Tissue and Average Tumor Reduction Baseline Residual Plasma ctDNAMutation in TMB Plasma MSI Case Collection Level at Burden Levels on MSIAlleles on ID (Months) Baseline Status Treatment Status Treatment CS9410.6 1.3% TMB-Low No MSI-H No CS95 54.0 0.4% TMB-Low No MSS N/A CS9625.3 2.3% TMB-Low No MSS N/A CS97 4.9 7.1% TMB-High Yes MSI-H Yes CS9830.3 5.5% TMB-High Yes MSI-H Yes CS99 N/A 15.0% TMB-Low No MSS N/A CS0013.7 2.3% TMB-High Yes MSI-H Yes CS01 76.2 0.8% TMB-Low No MSS N/A CS020.0 0.0% TMB-Low N/A MSS N/A CS03 18.5 0.5% TMB-High Yes MSI-H Yes CS0448.8 2.3% TMB-Low No MSS N/A CS05 6.2 0.7% TMB-Low No MSI-H No CS06 16.34.6% TMB-High No MSI-H No CS07 16.3 7.9% TMB-High No MSI-H No CS08 N/A7.2% TMB-Low No MSI-H No CS09 N/A 1.1% TMB-Low No MSS N/A

TABLE 8 Comparison of Tumor Mutation Burden and Microsatellite Statusfor Patients Evaluated for Response to Immune Checkpoint Blockade TissuePlasma Response MSI-H MSS MSI MSS Complete Response 4 0 4 0 PartialResponse 2 0 1 1 Progressive Disease 5 4 3 6 Not Evaluable 1 0 1 0Tissue Plasma Response TMB-High TMB-Low TMB-High TMB-Low CompleteResponse 4 0 4 0 Partial Response 2 0 1 1 Progressive Disease 3 3 0 9Not Evaluable 1 0 1 0 TMB-H is classified as ≥10 mutations/Mbp sequencedfortissue and ≥50.8 mutations/Mbp sequenced for plasma

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Any and all references and citations to other documents, such aspatents, patent applications, patent publications, journals, books,papers, web contents, that have been made throughout this disclosure arehereby incorporated herein by reference in their entirety for allpurposes.

Although the present invention has been described with reference tospecific details of certain embodiments thereof in the above examples,it will be understood that modifications and variations are encompassedwithin the spirit and scope of the invention. Accordingly, the inventionis limited only by the following claims.

1. A method for determining a prognosis or therapeutic regimen responsefor a patient having cancer, the method comprising: (a) capturing targetcirculating tumor DNA (ctDNA) from a first liquid sample from thepatient at a first timepoint prior to the patient beginning atherapeutic regimen; (b) determining a baseline ctDNA level at the firsttimepoint using the ctDNA from (a); (c) capturing target ctDNA from asecond liquid sample from the patient at a second timepoint after thepatient begins the therapeutic regimen; (d) determining an on-treatmentctDNA level at the second timepoint using the ctDNA from (c); (e)capturing target ctDNA from additional liquid samples from the patientat one or more additional consecutive timepoints while the patientcontinues the therapeutic regimen; (f) determining the on-treatmentctDNA level for each additional liquid sample at each of the one or moreadditional consecutive timepoints using the ctDNA from (e); and (g)determining the prognosis or therapeutic regimen response for thepatient based on comparing the baseline ctDNA level with each of theon-treatment ctDNA levels, wherein the prognosis or therapeutic regimenresponse is positive if a >80% reduction is found in two or moreconsecutive on-treatment ctDNA levels when compared to the baselinectDNA level.
 2. The method of claim 1, wherein the patient has a cancerselected from pancreatic, colon, gastric, endometrial,cholangiocarcinoma, breast, lung, head and neck, kidney, bladder,prostate cancer, or hematopoietic cancers.
 3. The method of claim 1,wherein the baseline ctDNA level and each of the on-treatment ctDNAlevels are determined by measuring one or more serum tumor proteinbiomarkers, one or more microsatellite instability (MSI) alleles, or atumor mutation burden (TMB).
 4. The method of claim 3, wherein the serumtumor protein biomarkers are CEA and/or CA19-9.
 5. The method of claim1, wherein the prognosis or therapeutic regimen response is positive ifa >90% reduction is found in the two or more consecutive on-treatmentctDNA levels when compared to the baseline ctDNA level.
 6. The method ofclaim 5, wherein the baseline ctDNA level and each of the on-treatmentctDNA levels are determined by measuring one or more microsatelliteinstability (MSI) alleles or a tumor mutation burden (TMB).
 7. Themethod of claim 1, wherein the therapeutic regimen is a checkpointinhibitor regimen.
 8. A system for determining a prognosis ortherapeutic regimen response for a patient having cancer, the systemcomprising: one or more processors; and a non-transitory memory devicecontaining instructions which, when executed on the one or moreprocessors, cause the one or more processors to perform processesincluding: (a) determining a baseline ctDNA level at a first timepointusing target circulating tumor DNA (ctDNA) captured from a first liquidsample from the patient at the first timepoint prior to the patientbeginning a therapeutic regimen; (b) determining an on-treatment ctDNAlevel at a second timepoint using ctDNA captured from a second liquidsample from the patient at the second timepoint after the patient beginsthe therapeutic regimen; (c) determining the on-treatment ctDNA levelfor each additional liquid sample at each of one or more additionalconsecutive timepoints using ctDNA captured from each of the additionalliquid samples from the patient at the one or more additionalconsecutive timepoints while the patient continues the therapeuticregimen; and (d) determining the prognosis or therapeutic regimenresponse for the patient based on comparing the baseline ctDNA levelwith each of the on-treatment ctDNA levels, wherein the prognosis ortherapeutic regimen response is positive if a >80% reduction is found intwo or more consecutive on-treatment ctDNA levels when compared to thebaseline ctDNA level.
 9. The system of claim 8, wherein the patient hasa cancer selected from pancreatic, colon, gastric, endometrial,cholangiocarcinoma, breast, lung, head and neck, kidney, bladder,prostate cancer, or hematopoietic cancers.
 10. The system of claim 8,wherein the baseline ctDNA level and each of the on-treatment ctDNAlevels are determined by measuring one or more serum tumor proteinbiomarkers, one or more microsatellite instability (MSI) alleles, or atumor mutation burden (TMB).
 11. The system of claim 10, wherein theserum tumor protein biomarkers are CEA and/or CA19-9.
 12. The system ofclaim 8, wherein the prognosis or therapeutic regimen response ispositive if a >90% reduction is found in the two or more consecutiveon-treatment ctDNA levels when compared to the baseline ctDNA level. 13.The system of claim 12, wherein the baseline ctDNA level and each of theon-treatment ctDNA levels are determined by measuring one or moremicrosatellite instability (MSI) alleles or a tumor mutation burden(TMB).
 14. The system of claim 8, wherein the therapeutic regimen is acheckpoint inhibitor regimen.
 15. A computer-program product tangiblyembodied in a non-transitory machine-readable storage medium, includinginstructions configured to cause one or more data processors to performactions including: (a) determining a baseline ctDNA level at a firsttimepoint using target circulating tumor DNA (ctDNA) captured from afirst liquid sample from a patient at the first timepoint prior to thepatient beginning a therapeutic regimen; (b) determining an on-treatmentctDNA level at a second timepoint using ctDNA captured from a secondliquid sample from the patient at the second timepoint after the patientbegins the therapeutic regimen; (c) determining the on-treatment ctDNAlevel for each additional liquid sample at each of one or moreadditional consecutive timepoints using ctDNA captured from each of theadditional liquid samples from the patient at the one or more additionalconsecutive timepoints while the patient continues the therapeuticregimen; and (d) determining a prognosis or therapeutic regimen responsefor the patient based on comparing the baseline ctDNA level with each ofthe on-treatment ctDNA levels, wherein the prognosis or therapeuticregimen response is positive if a >80% reduction is found in two or moreconsecutive on-treatment ctDNA levels when compared to the baselinectDNA level.
 16. The computer-program product of claim 15, wherein thepatient has a cancer selected from pancreatic, colon, gastric,endometrial, cholangiocarcinoma, breast, lung, head and neck, kidney,bladder, prostate cancer, or hematopoietic cancers.
 17. Thecomputer-program product of claim 15, wherein the baseline ctDNA leveland each of the on-treatment ctDNA levels are determined by measuringone or more serum tumor protein biomarkers, one or more microsatelliteinstability (MSI) alleles, or a tumor mutation burden (TMB).
 18. Thecomputer-program product of claim 17, wherein the serum tumor proteinbiomarkers are CEA and/or CA19-9.
 19. The computer-program product ofclaim 15, wherein the prognosis or therapeutic regimen response ispositive if a >90% reduction is found in the two or more consecutiveon-treatment ctDNA levels when compared to the baseline ctDNA level. 20.The computer-program product of claim 19, wherein the baseline ctDNAlevel and each of the on-treatment ctDNA levels are determined bymeasuring one or more microsatellite instability (MSI) alleles or atumor mutation burden (TMB).